CN110232583B - Electric power market marginal price planning method considering carbon emission right - Google Patents
Electric power market marginal price planning method considering carbon emission right Download PDFInfo
- Publication number
- CN110232583B CN110232583B CN201910095733.0A CN201910095733A CN110232583B CN 110232583 B CN110232583 B CN 110232583B CN 201910095733 A CN201910095733 A CN 201910095733A CN 110232583 B CN110232583 B CN 110232583B
- Authority
- CN
- China
- Prior art keywords
- unit
- carbon emission
- power generation
- generation enterprise
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 130
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 130
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000010248 power generation Methods 0.000 claims abstract description 95
- 238000005457 optimization Methods 0.000 claims abstract description 47
- 238000004364 calculation method Methods 0.000 claims abstract description 12
- 238000005086 pumping Methods 0.000 claims description 26
- 230000005540 biological transmission Effects 0.000 claims description 19
- 230000005611 electricity Effects 0.000 claims description 19
- 230000009194 climbing Effects 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 4
- 230000009977 dual effect Effects 0.000 claims description 2
- 238000012546 transfer Methods 0.000 claims description 2
- 238000010977 unit operation Methods 0.000 claims 1
- 239000000243 solution Substances 0.000 description 19
- 230000006870 function Effects 0.000 description 10
- 230000008901 benefit Effects 0.000 description 6
- 238000004146 energy storage Methods 0.000 description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 239000003245 coal Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000012821 model calculation Methods 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Water Supply & Treatment (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method and a system for planning marginal price of an electric power market by considering carbon emission rights, wherein the method comprises the following steps: acquiring the total carbon emission amount available for distribution of a power generation enterprise through a terminal, and acquiring the carbon emission right distributed by a set of the power generation enterprise according to the total carbon emission amount; establishing a multi-objective optimization model containing double main bodies of the power grid company and the power generation enterprise, and solving a Pareto optimal solution of the multi-objective optimization model containing the double main bodies of the power grid company and the power generation enterprise by adopting an NNC (NNC) method; and establishing a clearing model with the minimum operation cost of the power grid company as a target, and determining the start-stop states of each power generation enterprise unit and each pumped storage unit in the clearing model by taking the result obtained by calculation of the multi-objective optimization model as a parameter to obtain the day-ahead electric power market marginal price considering the influence of the carbon emission right. The method can truly reflect the influence of the carbon emission right on the marginal price of the electric power market in the future.
Description
Technical Field
The invention relates to a marginal price planning method for an electric power market, in particular to a marginal price planning method for an electric power market considering a carbon emission right.
Background
With the continuous development of society, a series of ecological environmental problems emerge, and especially the greenhouse effect caused by the large amount of greenhouse gas emission represented by carbon dioxide is spread all over the world, which brings great challenges to the survival development of human beings. At present, carbon emission in the electric power industry of China accounts for about 40% of the total carbon emission in China, and the promotion and implementation of a carbon emission trading mechanism can have a great influence on the development of the electric power industry. Therefore, under the environment of a new revolution of power system innovation, considering the introduction of carbon emission trading and carbon emission right distribution mechanism, the method will bring significant influence on the power market to be established at the present stage of China, especially on the marginal price of the current spot market, and thus, deep research is urgently needed to be carried out
The current research on the influence of carbon emission rights on the day-ahead power market can be mainly divided into the following two categories: the first type is based on a classical Guno equilibrium model, modeling is respectively carried out on a conventional unit, a new energy generator set, an energy storage system and the like, so that a two-layer optimization model for market equilibrium is constructed, and then a method and a step for solving the electric power market equilibrium model considering carbon emission cost are further provided, so as to research the influence of the carbon emission cost on the electric power market equilibrium state and the strategic behaviors of market members; the second type is a safety constraint unit combination model comprehensively considering carbon emission right distribution, converts unit combination into a Mixed Integer Quadratic Programming (MIQP) problem, and discusses the influence of different carbon emission right distribution schemes on the start-stop state results of each unit of the system and the overall operation cost of the system. But the research on the influence of carbon emission rights on the marginal price of the power market in the day is still poor.
The power market equilibrium model which is established based on the classical Guno model in the game theory and considers the carbon emission cost can fully consider the benefit maximization and the operation constraint of various different units and the clearing condition of the market, but the market organization needs to obtain the inverse demand function of the market in advance to calculate and obtain the actual market marginal price. The safety constraint unit combination model established by considering different carbon emission right distribution schemes can effectively analyze the influence of the different carbon emission right distribution schemes on the unit combination result, the system operation cost and the benefit pattern change under the implementation of the carbon trading mechanism, but neglects the benefit conflict among different subjects, cannot make a scheduling result giving consideration to the different subjects, and is difficult to truly reflect the influence of the carbon emission right on the marginal price of the power market in the future.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power market marginal price planning method considering the carbon emission right so as to reflect the influence of the carbon emission right on the power market marginal price in the future more truly.
In order to achieve the purpose, the technical scheme of the invention is as follows:
acquiring the total carbon emission amount available for distribution of a power generation enterprise through a terminal, and acquiring the carbon emission right distributed by a set of the power generation enterprise according to the total carbon emission amount;
establishing a multi-target optimization model containing double main bodies of a power grid company and a power generation enterprise to solve the starting and stopping states and the output of each power generation enterprise unit, the output of each new energy source unit and the starting and stopping states and the output of each pumped storage unit within one day;
solving a Pareto optimal solution of a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise by adopting an NNC method;
and establishing a clearing model aiming at the minimum operation cost of the power grid company, determining the starting and stopping states of each power generation enterprise unit and each pumped storage unit in the clearing model by taking the result obtained by calculation of the multi-objective optimization model as a parameter, and simultaneously carrying out constraint control on the output of each power generation enterprise unit, the output of each new energy source unit and the output of each pumped storage unit to obtain the day-ahead electric power market price margin considering the influence of the carbon emission right.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the multi-target clearing problem of the double main bodies including the power grid company and the power generation enterprise, the invention respectively establishes a multi-target optimization model including the double main bodies including the power grid company and the power generation enterprise and a clearing model aiming at the minimum operation cost of the power grid company, fully considers the benefit balance among different main bodies in the optimization process, and can simply and reliably solve the day-ahead marginal price of the power market after considering the influence of the carbon emission right.
Drawings
Fig. 1 is a flowchart of a method for planning marginal price of an electric power market in consideration of carbon emission rights according to an embodiment of the present invention;
FIG. 2 is a multi-step pricing graph of a conventional unit;
FIG. 3 is a diagram of a normalized subspace of a dual target optimization problem;
FIG. 4 is a schematic diagram of an IEEE 39 node system including a new energy storage unit, a pumped storage unit, and an external network connection;
FIG. 5 is a graph of predicted output of two wind generating sets and two photovoltaic power generation systems versus daily load of the systems;
FIG. 6 is a tie-line injection power output plot;
FIG. 7 is a Pareto front chart;
fig. 8 is a graph showing a comparison of the output of the photovoltaic power generation system 1;
fig. 9 is a graph of output versus photovoltaic power generation system 2;
fig. 10 is a graph of the output of the wind turbine generator system 1 in comparison;
FIG. 11 is a graph illustrating the output of the wind turbine generator set 2 in comparison;
fig. 12 is a graph showing the output force comparison of the pumped-storage unit 1;
fig. 13 is a comparison of the output of the pumped storage group 2;
FIG. 14 is a graph of the output of a coal fired unit in the system versus time;
FIG. 15 is a graph of the comparison of marginal price in the power market at the present day;
FIG. 16 is a graph of the magnitude of change in marginal price for a power market day before taking into account the impact of carbon emissions.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and detailed description.
Example (b):
referring to fig. 1, the method for planning the marginal price of the power market in consideration of the carbon emission right according to the embodiment includes:
101, downloading and obtaining the total carbon emission amount available for distribution of a power generation enterprise from a server through a terminal, and obtaining the carbon emission right distributed by a set of the power generation enterprise according to the total carbon emission amount; specifically, in this example, the terminal is a computer.
102. Establishing a multi-target optimization model containing double main bodies of a power grid company and a power generation enterprise to solve the starting and stopping states and the output of each power generation enterprise unit, the output of each new energy source unit and the starting and stopping states and the output of each pumped storage unit within one day;
103. solving a Pareto optimal solution of a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise by adopting an NNC method;
104. and establishing a clearing model aiming at the minimum operation cost of the power grid company, determining the starting and stopping states of each power generation enterprise unit and each pumped storage unit in the clearing model by taking the result obtained by calculation of the multi-objective optimization model as a parameter, and simultaneously carrying out constraint control on the output of each power generation enterprise unit, the output of each new energy source unit and the output of each pumped storage unit to obtain the marginal price of the power market in the day before considering the influence of the carbon emission right.
Therefore, the method establishes a multi-objective optimization model containing the double main bodies of the power grid company and the power generation enterprise and a clearing model aiming at the minimum operation cost of the power grid company respectively, and compares the clearing price calculated by the clearing model with the clearing price without considering the carbon transaction and the carbon emission right distribution condition, so that the influence of the carbon emission right on the marginal price of the power market in the future is evaluated, and the influence of the carbon emission right on the marginal price of the power market in the future can be reflected more truly.
At present, the carbon emission right distribution release time scale of each industry is set to be year by the nation, and the carbon emission right distribution time scale is modified into one day by the method so as to further ensure the accuracy of the result. Before assigning carbon emission rights to each power generation enterprise, the total amount of carbon emissions available for assignment is first specified. At present, the most common determination method is to adopt the sum of carbon emissions of each power generation enterprise without considering the carbon transaction and the carbon emission right distribution as a reference value, and then consider the corresponding emission reduction coefficient on the basis, that is:
Q＝αEb (1)
in the formula, Q is the total carbon emission amount which can be distributed by all power generation enterprises in one day; ebThe total carbon emission of each power generation enterprise without considering carbon trading and carbon emission right distribution; alpha is an emission reduction coefficient.
At present, two schemes, namely historical carbon emission based and power generation performance standard based, are mainly adopted to distribute the carbon emission rights of each power generation enterprise. The method adopts a scheme based on historical carbon emission to distribute the carbon emission rights of each power generation enterprise, the number of the carbon emission rights which can be distributed and obtained by each power generation enterprise unit is in direct proportion to the proportion of the carbon emission to the total amount of the power generation enterprise unit without considering carbon transaction and carbon emission right distribution, namely:
in the formula (I), the compound is shown in the specification,
the carbon emission of the ith power generation enterprise unit is not considered under the conditions of carbon transaction and carbon emission right distribution; eqiThe carbon emission right distributed to the ith power generation enterprise unit.
When a multi-objective optimization model containing a power grid company and a power generation enterprise double main body is established, the method respectively takes the total operation cost formed by minimizing the sum of the power grid company electricity purchasing cost, positive and negative rotation standby cost, start-stop cost and no-load cost as a target one, takes the sum of carbon emission cost required to be paid by each power generation enterprise unit as a target two, simultaneously considers the start-stop cost constraint of the unit, the system power balance constraint, the upper and lower limits of output of the conventional unit, the climbing/landslide constraint of the unit, the minimum start-stop time constraint of the unit, the operation constraint of the pumped storage unit, the rotation standby constraint, the consistent constraint of the start-stop state of the gas-steam combined cycle unit, the network transmission constraint, the upper and lower limits of output of the new energy unit and the multi-section quotation constraint of the conventional unit, and performs on the start-stop state and output, And solving the output of each new energy unit, the starting and stopping states and the output of each pumped storage unit and the like.
Specifically, the total operating cost of the power grid company is minimized, wherein the operating cost includes electricity purchase cost, positive and negative rotation standby cost, start-stop cost and no-load cost, namely:
in the formula, T is the total number of time segments, wherein one day is divided into 24 time segments, and each time segment is 60 min; n is a radical of1The total number of the conventional units; k is the total number of the sections of the multi-section quotation of the conventional unit;
the electric energy quotation of the conventional unit i at the kth section in the t period is carried out;
representing the output of the conventional unit i at the kth section in the t period;
and
respectively quoting positive rotation reserve capacity and negative rotation reserve capacity of the conventional unit i in a t period; sui,tAnd sdi,tRespectively representing the positive and negative rotation reserve capacity provided by the conventional unit i in the time period t;
representing the no-load cost coefficient of the conventional unit i; i isi,tThe starting and stopping state of the conventional unit i in the t period is represented and is a variable of 0-1; ciU,tAnd CiD,tAre respectively conventional machinesThe startup and shutdown costs of group i at time t; n is a radical of2The total number of the pumping and storage units,
and
respectively quoting positive rotation standby capacity and negative rotation standby capacity of the pumping unit s in a t period; sus,tAnd sds,tRespectively representing the positive and negative rotation reserve capacity provided by the pumping unit s in the t period; csU,tAnd CsD,tThe starting cost and the stopping cost of the pumping unit s in the time period t are respectively, the operation of the pumping unit does not consume fuel, the power generation cost is zero, zero quotation is adopted in the day-ahead market for ensuring the preferential output of the pumping unit, and the quotation of the new energy unit is also set to be zero in the same way.
Minimizing the sum of the carbon emission costs to be paid by the individual power generation enterprise units, i.e.
In the formula, preFor the carbon emission price, the price is set to be 15 yuan/ton in the research of the method; ei,cThe total carbon emission of the conventional power generation enterprise unit exceeding the carbon emission right allocated to the conventional power generation enterprise unit in one day is in the form of a piecewise function, namely
In the formula, Ei,tThe carbon emission of the conventional unit i in the period t is as follows:
Ei,t＝βciPi,t (6)
in the formula, betaciAnd (4) the carbon emission intensity coefficient corresponding to the conventional unit i.
Wherein, the start-up and shutdown expense constraint of the unit comprises:
boot cost constraints, namely:
shutdown cost constraints, namely:
in the formula, KiAnd JiThe single startup and shutdown costs of the conventional unit i are respectively; ksAnd JsThe single startup and shutdown costs of the pumping unit s are respectively; i isi,t/Zs,tThe starting state value of the conventional unit i/pumping storage unit s is 1, and the stopping state value is 0 in the starting and stopping states of the conventional unit i/pumping storage unit s in the time period t.
The system power balance constraint is:
in the formula, N3The number of system and external network links; n is a radical of4The number of wind generating sets; n is a radical of5Is the number of photovoltaic power generation systems; n is a radical ofDThe number of load nodes; pw,tThe output of the w wind generating set in the time period t; pv,tThe output of the vth photovoltaic power generation system in the time period t; pj,tInjecting power to the system for the jth and external network connecting lines in a time period t; ppg,s,tAnd Ppp,s,tRespectively generating power and pumping power of the pumping storage unit s in a time period t, wherein the generating power is positive, and the pumping power is negative; pd,tIs the predicted value of the load node d in the time period t; pLoss,tFor the system grid loss of time period t, the method sets it to 0.075% of the total load power.
The upper and lower output limits of the conventional unit are constrained as follows:
Ii,tPi,min≤Pi,t≤Ii,tPi,max (10)
in the formula, Pi,minIs the minimum output of a conventional unit iForce, Pi,maxThe maximum output of the conventional unit i.
The unit climbing/landslide constraint is as follows:
considering that the unit does not exceed the minimum output of the unit during the first period of start-up or the last period of shutdown, the unit ramp/landslide constraint may be expressed as follows:
in the formula, ruiAnd rdiRespectively representing the climbing rate and the landslide rate of the unit i; t60 is an operating period of 60 min.
The unit minimum on-off time constraint comprises:
minimum on-time constraint, namely:
minimum down time constraints, namely:
in the formula of Ui/DiThe time interval which represents that the unit i must be started/stopped at the beginning of the scheduling cycle is determined by the state of the unit when the last scheduling cycle is finished; t ison_i/Toff_iThe minimum starting time/the minimum stopping time of the unit i; xon_i,0/Xoff_i,0For the time that unit i has been continuously powered on/off at the beginning of the scheduling period.
The operational constraints of the pumped-storage unit include:
and (3) restraining an upper limit and a lower limit of output:
in the formula, Ppg,s,maxAnd Ppp,s,maxRespectively the maximum power generation power and the maximum pumping power of the pumping storage unit s; zpg,s,tAnd Zpp,s,tThe power generation state and the water pumping state of the storage unit s in the time period t are respectively, the state is in a corresponding state when the value is 1, and the state is not in a corresponding state when the value is 0.
The complementary constraint of operating condition, namely the pumping unit can not be in the water pumping and power generation operating condition at the same time period, as follows:
Zs,t＝Zpg,s,t+Zpp,s,t≤1 (15)
in actual operation, the daily power balance constraint also needs to be satisfied, as follows:
where ξ is the conversion efficiency of the storage unit, which is usually 75%.
The rotational standby constraint includes:
enough system rotation spare capacity is reserved to cope with the influence of load prediction errors. The positive rotation reserve capacity is used to compensate the influence caused by underestimated system load, and the negative rotation reserve capacity is used to compensate the influence caused by overestimated load.
And (3) conventional unit rotation standby constraint:
in the formula, sui,tAnd sdi,tRespectively providing positive and negative rotation reserve capacities for the unit i in the time period t; t60 is the rotating standby response time of the unit, namely 60 min.
And (3) rotating standby constraint of the pumping unit:
the rotating standby requirements of the system are obtained by accumulating the rotating standby of all the units as follows:
in the formula, Su,tAnd Sd,tRespectively the positive and negative rotation reserve capacity of the system in the time period t; l isu% and Ld% is the demand coefficient of the load forecast deviation to the system positive and negative rotation reserve capacity respectively.
The gas-steam combined cycle unit comprises:
in the model, 2 sets of a set of gas-steam combined cycle unit are considered to be respectively scheduled, and the start-stop states of the set of gas-steam combined cycle unit are controlled to be consistent, namely:
Ii,t＝Ij,t (20)
in the formula Ii,t、Ij,tThe start-stop states of a large-capacity unit and a small-capacity unit of a set of gas-steam combined cycle unit are respectively set.
The new energy unit has the following upper and lower output limit constraints:
when the reserve capacity of the traditional generator set is insufficient at a certain moment in the power system or because of the limit of the transmission capacity of a line close to the grid-connected position of the new energy power generation system, wind abandon or light abandon is inevitable, so the new energy output constraint is expressed as follows:
in the formula (I), the compound is shown in the specification,
scheduling the maximum active output of the w wind generating set in the t time period;
the schedulable maximum active output of the pth photovoltaic power generation system in the tth time period;
the network transmission constraints are:
according to the direct current power flow model, the constraint of the transmission power of the line is as follows:
in the formula, NLThe number of the transmission lines is; gl,i、Gl,s、Gl,j、Gl,w、Gl,vAnd Dl,dRespectively representing active power transmission factors between the power transmission line l and a conventional unit i, between the power transmission line l and a pumped storage unit s, between the power transmission line l and an external network connecting line j, between the power transmission line l and an external network connecting line w, between the power transmission line l and a photovoltaic power generation system v, and between the power transmission line l and a load d; plmaxIs the maximum transmission capacity of the transmission line l;
the conventional unit multi-section quotation constraint is as follows:
the quoted price curve of the conventional unit is an increasing step curve as shown in fig. 2, wherein the sum of the bid quantities in each section is equal to the total output of the unit, so the following constraints need to be satisfied:
each section of electric quantity still needs to meet the restriction of upper and lower limits of section electric quantity:
in the formula, LiThe length of the multi-section quoted electricity quantity is obtained.
When the carbon trading and carbon emission right distribution conditions are not considered, the K section quoted price of the conventional unit i is as follows:
in the formula, ai、bi、ciThe fuel cost coefficient of a conventional unit i; pi,t' is the quoted price corresponding output of the conventional unit i time period t, namely:
when considering the role of the CET mechanism, if the carbon emissions generated by the conventional unit i exceed its allocated quota, the corresponding carbon emission cost will need to be paid. Therefore, the total cost of the conventional unit i will vary, which necessarily adjusts the strategy of the online quotation, and adds the carbon emission cost predicted according to the allocated quota in the quotation, such as:
to facilitate the study discussion, the above optimization model can be rewritten into the following compact form:
min(J1,J2) (28)
s.t.f(x1,x2)＝0 (29)
g(x1,x2)≤0 (30)
equation constraints in the model are represented by an equation (29), wherein the equation constraints comprise a system power balance constraint (9), a daily electric quantity balance constraint (16) of the pumped storage unit and a start-stop state consistency constraint (20) of the gas-steam combined cycle unit; the formula (30) represents inequality constraints in the model, and comprises start-up and shut-down cost constraints (7) - (8) of the unit, upper and lower limit constraints (10) of output of the conventional unit, a climbing/landslide constraint formula (11) of the unit, minimum start-up and shut-down time constraints (12) - (13) of the unit, operation constraints (14) - (15) of the pumped storage unit, rotary standby constraints (17) - (19), a network transmission constraint formula (22), an upper and lower limit constraint formula (21) of output of the new energy unit and a multi-section quotation constraint formula (24) of the conventional unit; x is the number of1Representing output vectors of a conventional unit, a pumped storage unit, a wind generating set and a photovoltaic power generation system; x is the number of2And representing the start-stop state vectors of the conventional unit and the pumped storage unit.
For the multi-objective optimization model comprising double subjects of the power grid company and the power generation enterprise, which is provided by the method, the current common method in engineering is a weighted sum method, namely, a multi-objective problem is converted into a single-objective problem in a weighted sum mode, the weight of each objective is determined according to experience, but under complex conditions, a proper weight is often difficult to determine by experience. Theoretically, the optimal solution of the multi-objective problem is a set of optimal solutions, which is also called Pareto solutions (or Pareto fronts), so that the Pareto solutions of the multi-objective problem can be solved first, and then suitable compromise optimal solutions are selected according to actual conditions. The invention provides a method for solving the uniformly distributed optimal solution set by adopting an NNC method, and the optimal compromise solution can be effectively selected.
The basic principle of the NNC method is as follows: constraint conditions are added in an original multi-objective optimization model to define a feasible domain for target space optimization, so that a multi-objective optimization problem is converted into a series of single-objective optimization problems. Therefore, the optimal solution for each single-target problem is the point on the Pareto front. The steps for solving the Pareto frontier of the multi-objective optimization model containing the double main bodies of the power grid company and the power generation enterprise, which are provided by the invention, by adopting the NNC method are as follows:
1) according to constraints (29), (30), a minimum total operating cost J of the grid company is constructed1To obtain J by calculation1Minimum value of (J)1minAt the moment, the sum of the carbon emission cost required to be paid by each power generation enterprise unit can be correspondingly calculated to be J2max。
2) Constructing a single-target optimization problem for minimizing the sum J2 of the carbon emission cost required to be paid by each power generation enterprise unit according to the constraints (29) and (30), and calculating to obtain J2Minimum value of (J)2minAt the moment, the total operation cost J of the power grid company can be correspondingly calculated1max。
3) J calculated by steps 1) and 2)1min、J1max、J2min、J2maxThe target function (28) is normalized by:
as shown in fig. 3, to
As the abscissa, in
As ordinate, point A1(0,1) and A2(0,1) respectively corresponding to the point when the objective function J1 and the scalar function J2 on the normalized plane take the minimum value, and the two points respectively corresponding to the two optimal solutions G obtained by the solution in the steps 1) and 2)1(J1min,J2max) And G2(J1max,J2min). Connection point A1(0,1) and A2(0,1) derived line segment
Referred to as the Utopia line.
4) Equally dividing the Utopia line into m segments can be used to obtain (m +1) evenly distributed dividing points on the Utopia line
Then define from
Then the division point
Comprises the following steps:
5) at each division point
Is taken as the normal of the Utopia line
Which intersects the Pareto front at Bj. At this time, the following single-objective optimization problem can be constructed to solve the point Bj on the Pareto frontier by combining the original problem constraint compact forms (29) and (30):
minJ2 (33)
s.t.f(x1,x2)＝0 (34)
g(x1,x2)≤0 (35)
equation (36) may be such that the new solution space for constructing the single-objective optimization problem is located in the upper left region of the normal in fig. 2. At this time, the optimal solution B can be obtained by calling GAMS/CPLEX solverj. And step 5) is repeatedly executed, so that the uniformly distributed points on the series of Pareto fronts can be obtained through solving, and the uniformly distributed Pareto fronts can be described.
The method is characterized in that a direct current power flow model is used, the day-ahead market clearing model is established by taking the minimum total operation cost of a power grid company as a target, the starting and stopping states of each power generation enterprise unit and each water pumping energy storage unit in the clearing model are determined by taking the result obtained by calculating a multi-objective optimization model containing the power grid company and the power generation enterprise dual-body as a parameter, meanwhile, the output of each power generation enterprise unit, the output of each new energy source unit and the output of each water pumping energy storage unit are subjected to constraint control, and the day-ahead power market marginal price of the power in consideration of the influence of carbon emission is further obtained.
The objective function of the clearing model is consistent with the objective function J1 of the multi-objective optimization model containing the double main bodies of the power grid company and the power generation enterprise, namely, the objective function is the objective function which is used for minimizing the total operation cost of the power grid company, as shown in the formula (3).
The constraint conditions comprise unit startup and shutdown cost constraints (7) to (8), system power balance constraints (9), conventional unit output upper and lower limit constraints (10), unit climbing/landslide constraints (11), unit minimum startup and shutdown time constraints (12) to (13), operation constraints (14) to (16) of the pumped storage unit, rotation standby constraints (17) to (19), gas-steam combined cycle unit startup and shutdown state consistency constraints (20), new energy unit output upper and lower limit constraints (21), network transmission constraints (22) and conventional unit multi-section quotation constraints (24) in a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise, and optimization model result constraints shown in formulas (37) to (40) need to be added.
(1) Optimizing model result constraints
By solving a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise, the output of each power generation enterprise unit, the output of each new energy source unit and the output of each pumped storage unit can be determined; when the clear model is used for solving, the calculation result needs to be consistent with the calculation result in the optimization model, namely:
Pi,t.l-≤Pi,t≤Pi,t.l+ (37)
Pw,t.l-≤Pw,t≤Pw,t.l+ (38)
Pv,t.l-≤Pv,t≤Pv,t.l+ (39)
(Ppg,s,t+Ppp,s,t).l-≤Ppg,s,t+Ppp,s,t≤(Ppg,s,t+Ppp,s,t).l+ (40)
in the formula, Pi,t.l、Pw,t.l、Pw,tL and (P)pg,s,t+Ppp,s,t) L is respectively the output of the conventional unit i in the time period t, the output of the wind generating set w in the time period t, the output of the photovoltaic power generation system v in the time period t and the output of the pumped storage unit s in the time period t which are calculated in the multi-objective optimization model; for relaxation error, the present invention is set to 0.0000001.
In addition, the current market clearing model adopts a node electricity price pricing mechanism to form the node electricity price of every 60 minutes, and the calculation formula is as follows:
in the formula, LMPk,tThe node electricity price of the node k in the time period t; lambda [ alpha ]tLagrange multipliers which are system power balance constraints in the time period t;
a lagrange multiplier for the maximum forward power flow constraint of the line l;
a lagrange multiplier for the maximum reverse power flow constraint of the line l; gl,kThe generator output power transfer profile factor for generator node k to line l.
As can be seen from equation (41), when the network transmission constraint is ignored, the node electricity prices of the entire system are the same. When network transmission constraints are considered and congestion occurs in the system, the node electricity prices at both ends of the congestion line are different.
The constraint conditions and the objective functions in the day-ahead market clearing model described above are linear functions, and a CPLEX solver in GAMS can be used for solving.
Accordingly, the present embodiment also provides an electric power market marginal price planning system considering carbon emission rights, including:
the carbon emission right module is used for acquiring the total carbon emission amount available for distribution of the power generation enterprise and acquiring the carbon emission right distributed by the set of the power generation enterprise according to the total carbon emission amount;
the power grid company total operation cost module is used for establishing a target model for minimizing the total operation cost of the power grid company so as to obtain a total operation cost value of the power grid company;
the carbon emission cost sum module required to be paid by the power generation enterprise is used for establishing a carbon emission cost sum target model required to be paid by the minimum power generation enterprise set according to the obtained carbon emission rights distributed by the power generation enterprise set so as to obtain a carbon emission cost sum value required to be paid by the power generation enterprise;
and the electric power market marginal price module in the day ahead of the power day is used for taking the established target model of the total operation cost of the minimized power grid company as a market clearing model in the day ahead, and taking the obtained total operation cost value of the power grid company and the sum value of the carbon emission cost required to be paid by the power generation enterprises as parameters to obtain the electric power market marginal price in the day ahead of the power day in which the influence of the carbon emission right is considered.
Since the working principle of each module is the same as that of the method, it is not described in detail in this embodiment.
To further illustrate the utility and technical effects of the present invention, the following description is made in conjunction with an exemplary test:
the experimental example test adopts an IEEE 39 node system comprising a new energy source unit, a water pumping energy storage unit and an external network connecting line, as shown in figure 4, the system is connected with two wind generating sets at nodes 6 and 16, two photovoltaic generating sets at nodes 4 and 18, two water pumping energy storage units at nodes 13 and 23 and an external network connecting line at node 8. The power reference value is 100 MVA; a conventional unit 66.7% of the installed capacity is defined as a gas unit, and the rest is a coal-fired unit. The carbon emission intensity of the gas turbine set is set to be 0.5t/MW.h, and the carbon emission intensity of the coal turbine set is set to be 0.85 t/MW.h.
The grid-connected capacity of the two wind generating sets is respectively 360MW and 280MW, the grid-connected capacity of the two photovoltaic power generation systems is respectively 100MW and 120MW, and the grid-connected capacity of the two pumped storage sets is both 350 MW. Meanwhile, the peak load and the valley load of the system are set to be 6150MW and 4611MW respectively, and the curves of the predicted output of the two wind generating sets and the two photovoltaic power generation systems and the daily load of the system are shown in FIG. 5; the tie-line injection power output curve is shown in fig. 6.
And solving the multi-target optimization model containing the double main bodies of the power grid company and the power generation enterprise by adopting an NNC method. Taking m as 40, equally dividing the Utopia line into 40 segments, and calling a GAMS/CEPLEX solver to solve the Pareto frontier of the optimization problem, wherein the result is shown in FIG. 7.
Because the decision maker of the unit combination problem is a power grid company, and the primary objective of the multi-objective optimization model is to minimize the total operation cost of the power grid company, a proper point optimal solution can be screened out more intuitively from the Pareto frontier point set. In this case, selection
As a compromise optimal solution. Then, the starting and stopping states and the output of the generator set of each power generation enterprise obtained in the optimal solution are increasedAnd the output of the small and new energy source units and the start-stop state and the output of each pumped storage unit are used as parameters and are substituted into a clear model for solving. The two model calculation results are shown in table 1 based on the optimization model calculation results.
TABLE 1 comparison of the results of the two model calculations
According to the comparison in table 1, the calculated power grid operation cost and carbon cost of the two models are very close, and the error is controlled to be 10-7Within the order of magnitude, the calculation results of the two are considered to be consistent. Fig. 8-13 are output conditions calculated by the new energy unit and the pumped storage unit in two models respectively; FIG. 14 is a graph of the output of a coal fired unit in the system versus the output of the system.
From the above analysis, the calculation results of the optimization model and the clearing model are consistent, the clearing model is adopted to calculate and obtain the day-ahead electric power market marginal price considering the influence of the carbon emission right, and the clearing price is compared with the day-ahead electric power market marginal price calculated without considering the carbon trading and the carbon emission right distribution condition. The results of calculation without considering the carbon trading and carbon emission right assignment were used as a reference in the study, and the comparative cases are shown in fig. 15 and 16.
As can be seen from fig. 15 and 16, the clearing price of the electricity market changes after considering the influence of the carbon emission right, the electricity price rises in a part of time period compared with the electricity price without considering the influence of the carbon emission right, and the highest rise amplitude of the electricity price is 26.555%; the electricity price ratio is reduced after the influence of the carbon emission right is not considered in a part of time period, the highest reduction amplitude of the electricity price is-7.820%, and the change trend of the electricity price is the same as that of the prior art.
In summary, compared with the prior art, the invention has the following advantages:
1) the method is characterized in that the day-ahead electric power market clearing problem considering the influence of the carbon emission right is described as a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise and a clearing model aiming at the minimum operation cost of the power grid company, so that the benefit conflict between the power grid company and the power generation enterprise can be well balanced, and the day-ahead electric power market marginal price considering the influence of the carbon emission right can be calculated by modifying the conventional day-ahead electric power market clearing model.
2) The method can split the multi-objective complex optimization problem of the current power market in consideration of the influence of the carbon emission right into two optimization problems, namely: the machine combination problem and the economic dispatching problem effectively solve the problem that the original complex multi-target optimization problem cannot directly solve the day-ahead marginal price of the electric power market.
3) The NNC method is adopted to solve the multi-target optimization model containing the double main bodies of the power grid company and the power generation enterprise, the compromise solution in the optimal solution set can be visually and effectively selected, and the difficulty in selecting the weight coefficient by adopting the weighted sum method is avoided.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (4)
1. A method for planning marginal price of electric power market in consideration of carbon emission right, comprising:
acquiring the total carbon emission amount available for distribution of a power generation enterprise through a terminal, and acquiring the carbon emission right distributed by a set of the power generation enterprise according to the total carbon emission amount;
establishing a multi-target optimization model containing double main bodies of a power grid company and a power generation enterprise to solve the starting and stopping states and the output of each power generation enterprise unit, the output of each new energy source unit and the starting and stopping states and the output of each pumped storage unit within one day;
solving a Pareto optimal solution of a multi-objective optimization model containing double main bodies of a power grid company and a power generation enterprise by adopting an NNC method;
establishing a clearing model with the minimum operation cost of a power grid company as a target, determining the starting and stopping states of each power generation enterprise unit and each pumped storage unit in the clearing model by taking the result obtained by calculation of the multi-objective optimization model as a parameter, and simultaneously carrying out constraint control on the output of each power generation enterprise unit, the output of each new energy source unit and the output of each pumped storage unit to obtain the day-ahead electric power market price margin considering the influence of carbon emission right;
the multi-objective optimization model comprising the power grid company and the power generation enterprise is used for aiming at minimizing the total operation cost formed by the sum of the power grid company electricity purchasing cost, the positive and negative rotation standby cost, the starting and stopping cost and the no-load cost as one objective, and is used for aiming at minimizing the sum of the carbon emission cost required to be paid by each power generation enterprise unit as another objective;
the model of the object one is:
in the formula:
t is the total time period number, and each time period is 60min if the model is divided into 24 time periods in one day; n is a radical of1The total number of the conventional units; k is the total number of the sections of the multi-section quotation of the conventional unit;
the electric energy quotation of the conventional unit i at the kth section in the t period is carried out;
representing the output of the conventional unit i at the kth section in the t period;
and
respectively quoting positive rotation reserve capacity and negative rotation reserve capacity of the conventional unit i in a t period; sui,tAnd sdi,tRespectively representing the positive and negative rotation reserve capacity provided by the conventional unit i in the time period t;
representing the no-load cost coefficient of the conventional unit i; i isi,tThe starting and stopping state of the conventional unit i in the t period is represented and is a variable of 0-1; ciU,tAnd CiD,tRespectively the starting cost and the stopping cost of the conventional unit i in a time period t; n is a radical of2The total number of the pumping and storage units,
and
respectively quoting positive rotation standby capacity and negative rotation standby capacity of the pumping unit s in a t period; sus,tAnd sds,tRespectively representing the positive and negative rotation reserve capacity provided by the pumping unit s in the t period; csU,tAnd CsD,tRespectively starting up and stopping the pumping unit s at the time t;
the model of the second target is as follows:
in the formula, preIs the carbon emission price; ei,cThe total carbon emission of the conventional power generation enterprise unit exceeding the carbon emission right allocated to the conventional power generation enterprise unit in one day is in the form of a piecewise function, namely
In the formula, EqiThe carbon emission right distributed to the ith power generation enterprise unit; ei,tThe carbon emission of the conventional unit i in the period t is as follows:
Ei,t＝βciPi,t
in the formula, betaciAnd (4) the carbon emission intensity coefficient corresponding to the conventional unit i.
2. The method for planning marginal price of electric power market considering carbon emission right according to claim 1, wherein the multiobjective optimization model including the grid company and the power generation enterprise dual bodies is further provided with constraint conditions, and the constraint conditions include: the system comprises a unit, a system power balance constraint, a conventional unit output upper and lower limit constraint, a unit climbing/landslide constraint, a unit minimum start-up and shut-down time constraint, a pumped storage unit operation constraint, a rotary standby constraint, a gas-steam combined cycle unit constraint, a new energy unit output upper and lower limit constraint, a network transmission constraint and a conventional unit multi-section quotation constraint.
3. The method for planning marginal price of electric power market in consideration of carbon emission right according to claim 1, wherein the carbon emission right allocated to the power generation enterprise unit is obtained by the following formula:
in the formula:
Q＝αEb
in the formula:
Ebthe total carbon emission of each power generation enterprise without considering carbon trading and carbon emission right distribution; alpha is an emission reduction coefficient.
4. The method for planning marginal price of electric power market considering carbon emission right according to claim 1, wherein the clearing model adopts a node electricity price pricing mechanism to form a node electricity price every 60 minutes, and the calculation formula is as follows:
in the formula, LMPk,tThe node electricity price of the node k in the time period t; lambda [ alpha ]tLagrange multipliers which are system power balance constraints in the time period t;
a lagrange multiplier for the maximum forward power flow constraint of the line l;
a lagrange multiplier for the maximum reverse power flow constraint of the line l; gl,kThe generator output power transfer profile factor for generator node k to line l.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910095733.0A CN110232583B (en) | 2019-01-31 | 2019-01-31 | Electric power market marginal price planning method considering carbon emission right |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910095733.0A CN110232583B (en) | 2019-01-31 | 2019-01-31 | Electric power market marginal price planning method considering carbon emission right |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110232583A CN110232583A (en) | 2019-09-13 |
| CN110232583B true CN110232583B (en) | 2021-01-05 |
Family
ID=67860367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910095733.0A Active CN110232583B (en) | 2019-01-31 | 2019-01-31 | Electric power market marginal price planning method considering carbon emission right |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110232583B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110956504B (en) * | 2019-12-02 | 2022-06-07 | 江苏方天电力技术有限公司 | Contract electric quantity optimization decomposition method considering carbon emission influence |
| CN113763206A (en) * | 2021-09-10 | 2021-12-07 | 国网上海市电力公司 | Power grid low-carbon scheduling method based on unit carbon emission characteristics |
| CN113763219A (en) * | 2021-09-14 | 2021-12-07 | 瑞格人工智能科技有限公司 | Carbon index compiling method |
| CN115859691B (en) * | 2023-02-21 | 2023-05-05 | 国网浙江省电力有限公司金华供电公司 | Multi-objective optimal scheduling method for electric heating combined demand response |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU6778200A (en) * | 1999-10-08 | 2001-04-23 | Automated Power Exchange, Inc. | The virtual trading floor for trading fungible, ephemeral commodities including electric energy |
| CN103337039B (en) * | 2013-04-28 | 2016-05-25 | 国家电网公司 | A kind of medium-term and long-term power purchase decision-making technique of multiple target multiple constraint |
| CN105844367A (en) * | 2016-04-08 | 2016-08-10 | 国电南瑞科技股份有限公司 | Sensitivity factor based calculating method for power grid node marginal pricing |
| CN107833101B (en) * | 2017-11-10 | 2021-08-27 | 国家电网公司西北分部 | Method for calculating transaction cost of renewable energy based on node electricity price mechanism |
-
2019
- 2019-01-31 CN CN201910095733.0A patent/CN110232583B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110232583A (en) | 2019-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110232583B (en) | Electric power market marginal price planning method considering carbon emission right | |
| Yang et al. | Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing | |
| CN113452020B (en) | Scheduling method of electric hydrogen energy system considering flexible hydrogen demand | |
| Hamilton et al. | Dispatch optimization of concentrating solar power with utility-scale photovoltaics | |
| CN105207259B (en) | Micro-grid system dispatching method under based on energy management and net state | |
| Chen et al. | A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market | |
| CN112103943B (en) | Safety checking method and device for delivery of electric power spot market in the day-ahead and storage medium | |
| CN105139147A (en) | Economic scheduling method for micro-grid system | |
| CN109586284B (en) | Random production simulation method of transmitting-end power system considering energy curtailment and application | |
| CN114498639B (en) | Day-ahead scheduling method of multi-microgrid combined mutual aid considering demand response | |
| CN110535183B (en) | Scheduling method and system of virtual power plant | |
| CN111210079B (en) | Operation optimization method and system for distributed energy virtual power plant | |
| Fernández‐Muñoz et al. | Contribution of non‐conventional pumped‐storage hydropower plant configurations in an isolated power system with an increasing share of renewable energy | |
| CN112700066A (en) | Optimal time scale coordination method for scheduling of electric-thermal integrated energy system | |
| CN112308411A (en) | Comprehensive energy station random planning method and system based on dynamic carbon transaction model | |
| CN110247392B (en) | Multi-standby resource robust optimization method considering wind power standby capacity and demand side response | |
| Rizvi et al. | Optimal scheduling of virtual power plants utilizing wind power and electric vehicles | |
| CN112510690B (en) | Optimal scheduling method and system considering wind-fire-storage combination and demand response reward and punishment | |
| CN111724254B (en) | Peak regulation auxiliary service and electric energy combined clearing method, system, device and medium | |
| CN111898801A (en) | Method and system for configuring multi-energy complementary power supply system | |
| CN115986812A (en) | Micro-grid economic planning method and device considering energy storage and demand response | |
| CN113629781A (en) | Multi-VPP day-ahead coordinated optimization method and device based on EMPC | |
| CN114676979A (en) | Energy scheduling method and device, computer equipment and storage medium | |
| Azaroual et al. | Model predictive control-based energy management strategy for grid-connected residential photovoltaic–wind–battery system | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |