CN115034549A - Micro-grid energy scheduling optimization method based on improved symbiont search - Google Patents
Micro-grid energy scheduling optimization method based on improved symbiont search Download PDFInfo
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Abstract
The invention discloses an energy scheduling optimization method based on improved symbiont search and suitable for a microgrid, which comprises the following steps: designing a structure of an island/grid-connected microgrid based on a multi-agent system, establishing a mathematical model of an improved symbiotic search algorithm, establishing a mathematical model of energy scheduling optimization of an island/grid-connected microgrid, and designing an energy scheduling optimization process of the island/grid-connected microgrid based on the improved symbiotic search algorithm. Improving the diversity of organisms by introducing recombination and mutation stages by adopting an improved symbiont search algorithm; establishing an economic objective function, an optimization variable and a power balance constraint condition containing the state information 0/1 of the public connection point; the multi-agent solves the mathematical model for the energy scheduling optimization of the island/grid-connected micro-grid by collecting data information and utilizing improved symbiont search. The method can effectively improve the economic benefit in the MG energy scheduling optimization and the stability of the optimization result.
Description
Technical Field
The invention belongs to the technical field of intelligent power grids and intelligent optimization algorithms, and relates to a Micro Grid (MG) energy scheduling optimization method based on Improved Symbiotic Organism Search (ISOS).
Background
The energy scheduling optimization of the MG through an intelligent optimization algorithm is an important guarantee for ensuring that the MG runs in the most economic and environment-friendly state. However, different intelligent optimization algorithms applied to MG energy scheduling may yield different results. The intelligent optimization algorithms which are used in MG energy scheduling optimization include Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial bee colony Algorithm (ABC), and the like. However, GA, PSO, and ABC require more parameters to control, and may cause the speed of optimization to decrease rapidly and the probability of getting into a local optimum to increase rapidly due to the increase in complexity of the optimization problem. A 24-hour energy scheduling optimization for an MG typically involves hundreds of optimization variables. Therefore, the results obtained after the MG performs energy scheduling optimization each time using GA, PSO, or ABC are unstable. Compared with GA, PSO and ABC, the symbiont search algorithm (SOS) has the characteristics of simplicity in operation, fewer control parameters, high stability, strong optimization capability and capability of obtaining a better optimization result. However, although the application of SOS to MG energy scheduling optimization can obtain better optimization results than GA, PSO, and ABC, SOS also has a situation of falling into a local optimum value to cause instability of the optimization results.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a microgrid energy scheduling optimization method based on improved symbiotic search, which can improve the global search capability and stability of SOS and improve the economic benefit and stability of optimization results when the method is applied to MG energy scheduling optimization.
In order to solve the technical problems, the invention adopts the following technical scheme.
The invention discloses a micro-grid energy scheduling optimization method based on improved symbiont search, which is characterized by comprising the following steps of:
the islanding/grid-connected microgrid refers to: the micro-grid can work in an island mode or a grid-connected mode, and the micro-grid can realize dynamic switching in the island mode and the grid-connected mode by controlling the on-off state of a public connection point through a public connection point intelligent agent.
Further, the specific process of designing the island/grid-connected microgrid structure based on the multi-agent system in the step 1 is as follows:
the island/grid-connected micro-grid structure based on the multi-agent system comprises renewable energy sources, a storage battery, a diesel engine, a load, a public connection point between the micro-grid and a power distribution network and the power distribution network, wherein the renewable energy sources, the storage battery, the diesel engine and the load are connected with one another through power transmission lines, and the power distribution network is connected with the renewable energy sources, the storage battery, the diesel engine and the load through the public connection point between the micro-grid and the power distribution network; the renewable energy sources, the storage battery, the diesel engine, the load, a common connection point between the microgrid and the power distribution network are respectively connected with corresponding intelligent bodies, wherein the renewable energy sources, the storage battery, the diesel engine, the load, the common connection point between the microgrid and the power distribution network and the intelligent bodies of the power distribution network are connected with the intelligent bodies of the microgrid; the intelligent agent connected with the renewable energy source is used for collecting power output information of the renewable energy source and sending an instruction whether the renewable energy source needs to be abandoned to the renewable energy source; the intelligent agent connected with the storage battery is used for collecting the charge state information of the storage battery and sending a charging/discharging power instruction to the storage battery; the intelligent agent connected with the diesel engine is used for collecting the upper and lower limit information of the output power of the diesel engine and sending an output power instruction to the diesel engine; the intelligent agent connected with the load is used for collecting the load value at each moment and sending an instruction whether to unload the load or not to the load; the intelligent agent connected with the public connection point between the micro-grid and the power distribution network is used for collecting the on-off state information of the public connection point between the micro-grid and the power distribution network and sending a closing or opening instruction to the public connection point between the micro-grid and the power distribution network; the intelligent agent connected with the power distribution network is used for collecting the power price information of the power distribution network and sending whether power selling is needed or not to the power distribution network; the micro-grid intelligent body is used for energy scheduling optimization; the intelligent agents connected with the renewable energy sources, the storage battery, the diesel engine, the load, a public connection point between the micro-grid and the power distribution network send all collected data information to the micro-grid intelligent agents; the energy scheduling instruction obtained by optimizing the micro-grid intelligent agent is sent to the intelligent agent connected with the renewable energy source, the storage battery, the diesel engine, the load, the public connection point between the micro-grid and the power distribution network; the island/grid-connected micro-grid structure based on the multi-agent system considers that the micro-grid is dynamically switched and operated between an island mode and a grid-connected mode through controlling a public connection point between the micro-grid and a power distribution network by an agent, and the improvement of the economic benefit of the micro-grid is facilitated.
Further, the step 2 of establishing a mathematical model for improving a symbiont search algorithm comprises establishing mathematical models of a mutual benefit stage, a partial benefit symbiosis stage, a parasitic stage, a recombination stage and a variation stage, and the specific process comprises the following steps:
2.1 mutualistic symbiotic phase
The purpose of the mutualism symbiosis stage is to increase the mutual survival advantage of the two organisms in the ecosystem; new candidate solutions
And
is based on the organism X n And X m The reciprocal mutual profit generation is represented by formula (1) and formula (2); according to this rule, organism X n And X m The updating is performed only if their new fitness is better than the fitness of the previous iteration:
B 1 ,B 2 ∈{1,2} (4)
wherein N, m belongs to {1,2, …, N } (N is the population size), N ≠ m, and rand (0,1) is a random value between 0 and 1; x best Is the optimal individual for the current iteration, M v Is a "mutual advantage vector" of a relationship characteristic between two organisms, B 1 And B 2 Is a random number in the set {1,2} respectively, and represents the mutual income factor of the mutualistic symbiotic organisms;
2.2 paragenetic symbiosis
New candidate solutions
According to the organism X n And X m The paragenetic symbiosis of (a) is represented by formula (5); according to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration:
wherein rand (-1,1) is a random number between-1 and 1;
2.3 parasitic
In improving symbiont search algorithm, X is randomly selected n Randomly modifying the parameters of partial dimensionality to obtain a variant individual called parasitic vector denoted as X pv (ii) a Then randomly selecting an organism X from the ecological system m (n ≠ m) as X pv The host of (1); calculating and comparing the fitness value of the parasitic vector and the fitness value of the host, and if the fitness value of the parasitic vector is better, then the organism X m Will be replaced, otherwise X m Will be immune, continue to survive and remain in the population;
2.4 recombination phase
In ecosystems, consider living beingsThe new organism generated by the somatic reproduction has the gene recombination condition; random selection of X m The parameter of partial latitude in (1) is used to replace X n Obtaining a new candidate solution by using the parameters of the same latitude
It is considered as a new organism
And gene recombination occurs; according to this rule, organism X n Performing an update only if its new fitness is better than the fitness of the previous iteration;
2.5 mutation stage
In an ecosystem, the existence of genetic variation in a new organism generated by organism reproduction is considered; original organism X n Due to the reception of organism X m And X k (k ≠ m) novel organisms that cause reproduction after long-term influence
A gene mutation is generated, represented by formula (6); according to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration:
F∈[0,1] (7)
wherein N, m, k belongs to {1,2, …, N } and N ≠ m ≠ k, F is a variation factor and usually takes a value of 0.3-0.7, the magnitude of the variation factor controls the magnitude of the new organism variation, and the larger the value is, the larger the magnitude of the new organism variation is; conversely, the smaller the variation amplitude.
Further, the step 3 of establishing a mathematical model of the islanding/grid-connected microgrid energy scheduling optimization, which includes an objective function and an optimization variable of the islanding/grid-connected microgrid energy scheduling optimization and a power balance constraint of the islanding/grid-connected microgrid in the energy scheduling optimization, and the specific process includes:
the mathematical model for the energy scheduling optimization of the island/grid-connected micro-grid needs to consider a dynamic switching model of the micro-grid between an island mode and a grid-connected mode; the aim of the energy scheduling optimization of the island/grid-connected micro-grid is to minimize the total operation cost, namely to maximize the economic benefit; therefore, the objective function of the energy scheduling optimization of the island/grid-connected micro grid is defined as follows
τ t ∈{0,1} (9)
In the formula, C MG Is the total operating cost of the energy scheduling optimization of the island/grid-connected micro-grid, T is the total energy scheduling time,
and
the operating costs of the diesel engine and the battery, respectively;
the cost of giving up renewable energy is represented, and the economic loss is caused by giving up part of renewable energy when the power of the island micro-grid is excessive;
the unloading cost is expressed, and is economic loss caused by the fact that partial load needs to be unloaded when the power of the island micro-grid is insufficient;
and
the electricity purchasing cost and the electricity selling cost from the grid-connected micro-grid to the power distribution network are respectively tau t The state information of the public connection point between the micro-grid and the distribution network; when tau is measured t When the number is equal to 1, the alloy is put into a container,the common connection point between the microgrid and the power distribution network is in a closed state, the microgrid is connected with the power distribution network at the moment, the microgrid is in a grid-connected mode, and the cost of giving up renewable energy sources and the unloading cost are not taken into account in the total running cost; when tau is t When the total operation cost does not account for the electricity purchasing cost and the electricity selling cost, a public connection point between the micro-grid and the power distribution network is in a disconnection state, the micro-grid is disconnected with the power distribution network at the moment, the micro-grid is in an island mode, and the total operation cost does not account for the electricity purchasing cost and the electricity selling cost;
the optimization variables of the island/grid-connected microgrid in the improved symbiotic organism searching algorithm comprise the output power of a diesel engine, the charging and discharging power of a storage battery, the quantity of abandoned renewable energy sources, the unloading quantity, the quantity of electricity purchased and sold by the microgrid to a power distribution network and the state change of a public connection point between the microgrid and the power distribution network, so the optimization variables of the island/grid-connected microgrid are defined as follows
In the formula, X n Is an optimized variable P of an island/grid-connected micro-grid in an improved symbiotic organism search algorithm t CDG Is the output power of the diesel engine, P t B+ And P t B- Respectively the charging and discharging power of the accumulator, P t abRES And P t unLoad Respectively the abandoned renewable energy quantity and the unloading quantity, P, of the island micro-grid t Buy And P t Sell The electricity purchasing quantity and the electricity selling quantity from the grid-connected micro-grid to the power distribution network are respectively;
the method comprises the following steps that the constraint conditions of a diesel engine, a storage battery, renewable energy sources, loads, the quantity of abandoned renewable energy sources, the unloading quantity, the electricity purchasing quantity and the electricity selling quantity need to be met in energy scheduling optimization; in addition, the islanding/grid-connected microgrid also needs to satisfy power balance constraints in energy scheduling optimization, as shown in formula (11):
P t Load ＝P t RES +P t CDG +(P t B- -P t B+ )+τ t ·(P t Buy -P t Sell )-(1-τ t )·(P t abRES -P t unLoad ) (11)
in the formula, P t Load And P t RES Respectively, the user load and the renewable energy of the island/grid-connected micro-grid.
Further, the specific process of designing the island/grid-connected micro-grid energy scheduling optimization process based on the improved symbiont search algorithm in the step 4 is as follows:
the method comprises the step of solving an improved symbiont search algorithm when optimization is executed in the island/grid-connected microgrid energy scheduling, and the algorithm flow is as follows:
step 4.1: inputting data of renewable energy sources, loads and time-of-use electricity prices of an island/grid-connected micro-grid at the time T of 1,2, …;
step 4.2: ecosystem initialization for improving symbiont search algorithm: setting a population size N, a variable number D, a maximum iteration number G, a variation factor F and total energy scheduling time T;
step 4.3: initializing the population meeting the corresponding constraint condition: POP ═ X 1 ,X 2 ,…,X N )；
Step 4.4: let the iteration number be gen 1;
step 4.5: calculating an objective function, namely a fitness value, of the energy scheduling optimization of the island/grid-connected micro-grid according to the formula (8);
step 4.6: the N fitness values are sorted from large to small, and the corresponding optimal individual X when the fitness value is minimum is determined best ；
Step 4.7: let n equal to 1;
step 4.8: and (3) executing a mutual benefit symbiosis stage: random selection of two individuals X n And X m Generating a new individual according to the formula (1) and the formula (2)
And
if the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.9: and (3) executing a partial interest symbiosis stage: generation of novel entities according to equation (5)
If the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.10: and (3) executing a parasitic stage: random selection of X n Randomly modifying the parameters of partial dimensionality to obtain a parasitic vector X pv (ii) a Then random individual X m (n ≠ m) as X pv The host of (1); if the fitness value of the parasitic vector is better, then the individual X m Will be X pv Substitution;
step 4.11: and (3) executing a recombination stage: random selection of X m Partial latitude parameter replacement X in (1) n Obtaining a new individual by using the parameters of the same latitude
If the fitness value of the new individual is better than that of the original individual X n The original individual is updated to a new individual according to the fitness value;
step 4.12: performing a mutation phase: generation of novel entities according to formula (6)
If the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.13: let n be n + 1;
step 4.14: determine if N is greater than N? If not, skipping to step 4.8; if yes, executing step 4.15;
step 4.15: let iteration number gen be gen + 1;
step 4.16: determine if the iteration number gen is greater than the maximum iteration number G? If not, skipping to step 4.6; if yes, executing step 4.17;
step 4.17: outputting the optimal individual X best And the algorithm ends.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention designs an isolated island/grid-connected MG structure chart based on a Multi-agent system (MAS), connects a public connection Point (PCC) between the MG and a power distribution network with an agent, controls the on or off of the PCC by the agent to enable the MG to be in an isolated island or grid-connected mode, is beneficial to participating the on-off state information of the PCC into the establishment of an isolated island/grid-connected MG energy scheduling optimization mathematical model, and is beneficial to the MG to realize the dynamic switching between the isolated island and the grid-connected mode in the energy scheduling.
2. The ISOS model established by the invention considers the situations of gene recombination and mutation of new organisms generated by organism propagation in an ecosystem. Compared with SOS, ISOS is more beneficial to improving the diversity and global searching capability of organisms.
3. The mathematical model for the energy scheduling optimization of the island/grid-connected MG, which is established by the invention, comprises on/off state information which is expressed by 0/1 and represents PCC, so that the established mathematical model realizes the unified combination of the mathematical model for the energy scheduling optimization of the island MG and the mathematical model for the energy scheduling optimization of the grid-connected MG, and is beneficial to realizing the dynamic switching between the island MG and the grid-connected MG in the energy scheduling optimization process.
Drawings
Fig. 1 is a structural diagram of an island/grid-connected MG based on MAS according to an embodiment of the present invention.
Fig. 2 is a flowchart of ISOS-based isb/grid-connected MG energy scheduling optimization according to an embodiment of the present invention.
Fig. 3 is a simulation result diagram of ISOS and SOS-based isb/grid-connected MG energy scheduling optimization case according to an embodiment of the present invention.
Fig. 4 is a flowchart of an ISOS-based MG energy scheduling optimization method according to an embodiment of the present invention.
Detailed Description
The invention discloses a micro-grid energy scheduling optimization method based on improved symbiotic search, which comprises the design of an island/grid-connected micro-grid structure based on a multi-agent system, the establishment of a mathematical model of improved symbiotic search, the establishment of a mathematical model of island/grid-connected micro-grid energy scheduling optimization, and the design of an island/grid-connected micro-grid energy scheduling optimization flow chart based on improved symbiotic search. Improving symbiont search algorithms by introducing recombination and mutation phases to increase organism diversity; considering that the micro-grid can be dynamically switched between an island mode and a grid-connected mode, an economic objective function, an optimization variable and a power balance constraint condition containing public connection point state information 0/1 are established; the multi-agent solves the mathematical model for the energy scheduling optimization of the island/grid-connected micro-grid by collecting data information and utilizing improved symbiont search. The method can further improve the economic benefit in the MG energy scheduling optimization and the stability of the optimization result.
The description of the technical solution of the present invention relates to the following english abbreviations:
ISOS: improving symbiont search algorithms. And (4) SOS: symbiont search algorithms. MG: a microgrid. GA: and (4) genetic algorithm. PSO: and (4) performing particle swarm optimization. ABC: and (4) artificial bee colony algorithm. MAS Multi agent System. PCC: a point of common connection between the microgrid and the distribution grid. RES: a renewable energy source. BESS: and (4) a storage battery. CDG: a diesel engine. Load: and (4) loading. DN: a power distribution network. Agent: and (4) an intelligent agent. MGA: microgrid agent. DNA: distribution network agent. PCA: a point of common connection agent. RA: renewable energy agents. BA: battery agent. DA: a diesel engine agent. LA: and loading the intelligent agent.
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 4 is a flowchart of an ISOS-based MG energy scheduling optimization method according to an embodiment of the present invention. As shown in fig. 4, the method for optimizing MG energy scheduling based on the ISOS of the present invention includes the following steps:
The above-mentioned "island/grid-connected MG" means that the MG can operate in an island mode or a grid-connected mode, and the MG controls the on-off state of the PCC by using the PCA to realize dynamic switching between the island mode and the grid-connected mode.
The specific process of the step 1 is as follows: the island/grid-connected MG structure comprises RES, BESS, CDG, Load, PCC and DN, wherein RES, BESS, CDG and Load are connected with each other through a power transmission line, and DN is connected with RES, BESS, CDG and Load through PCC; RES, BESS, CDG, Load, PCC and DN are respectively connected with corresponding agents, wherein the agents of RES, BESS, CDG, Load, PCC and DN are connected with MG Agent (MGA); the Agent connected with the RES is used for collecting power output information of the RES and sending an instruction whether the RES needs to be abandoned or not to the RES; the Agent connected with the BESS is used for collecting information such as the charge state of the BESS and sending a charging/discharging power instruction to the BESS; the Agent connected with the CDG is used for collecting information such as the upper and lower limits of the output power of the CDG and sending an output power instruction to the CDG; the Agent connected with the Load is used for collecting the Load value at each moment and sending an instruction whether unloading is needed to the Load; the Agent connected with the PCC is used for collecting the switch state information of the PCC and sending a closing or opening instruction to the PCC; the Agent connected with the DN is used for collecting information such as the price of the DN electricity and sending whether the electricity needs to be purchased or not to the DN; the MGA is mainly responsible for energy scheduling optimization. The agents connected with the RES, the BESS, the CDG, the Load, the PCC and the DN need to send all collected data information to the MGA; and the energy scheduling instruction obtained by MGA optimization needs to be sent to the Agent connected with RES, BESS, CDG, Load, PCC and DN. According to the isolated island/grid-connected MG structure, the dynamic switching operation of the MG between an isolated island mode and a grid-connected mode is realized by considering the control of the PCC by the Agent, and the economic benefit of the MG is favorably improved.
Fig. 1 is a structural design diagram of an island/grid-connected MG based on MAS. Island/grid-connected MG structures include DN, PCC, CDG, BESS, RES, and Load. The PCC is a public connection point of interaction of the MG and the DN, and when the PCC is closed, the MG is switched to a grid-connected mode; when the PCC is disconnected, the MG switches to island mode. The MAS includes local Agents and MGAs. Local agents include Load Agent (LA), BESS Agent (BA), CDG Agent (DA), RES Agent (RA), PCC Agent (PCA), and DN Agent (DNA). The local Agent is responsible for collecting and classifying data information from loads, batteries, diesel engines, renewable energy sources, public connection points and distribution networks and sending it into the MGA. Wherein the PCA collects the switch state information of the public connection point; the DNA is collected with the information such as the purchase and sale electricity price of the distribution network. And after receiving the data information from the local Agent, the MGA uses the ISOS to execute MG layer optimization and sends the optimization result back to the local Agent. The local Agent will make an energy scheduling plan and transmit power commands for each unit according to the optimization results.
The specific process of the step 2 is as follows: the ISOS includes mathematical models of the mutual stage, bias stage, parasite stage, recombination stage, and mutation stage. Considering the genetic recombination and mutation of new organisms generated by the reproduction of organisms in an ecosystem, ISOS introduces recombination and mutation stages on the basis of SOS to improve the diversity of organisms. ISOS mimics the interaction strategy employed by symbionts for survival and reproduction in ecosystems. The ISOS consists of five phases: mutualistic symbiosis, parafunctional symbiosis, parasitism, recombination and mutation. The corresponding mathematical model is as follows:
2.1 mutualistic symbiotic phase
The purpose of the mutualistic symbiotic phase is to increase the mutual survival advantage of the two organisms in the ecosystem. New candidate solutions
And
is based on the organism X n And X m The reciprocal mutual benefit generation of (2) is represented by formula (1). According to this rule, organism X n And X m The updating is performed only if their new fitness is better than the fitness of the previous iteration.
B 1 ,B 2 ∈{1,2} (4)
In the formula, N, m belongs to {1,2, …, N } (N is the population size), and N ≠ m. rand (0,1) is a random value between 0 and 1. X best Is the optimal individual for the current iteration. M v Is a "mutual advantage vector" of the characteristics of a relationship between two organisms. B is 1 And B 2 Are random numbers in the set {1,2} respectively, and represent the mutual income factors of the mutualistic symbiotic organisms.
2.2 partiality to symbiosis
To be mutually beneficialSimilar in generation phase, new candidate solutions
According to the organism X n And X m The paragenetic symbiosis of (1) is represented by formula (5). According to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration.
In the formula, rand (-1,1) is a random number between-1 and 1.
2.3 parasitic
In ISOS, X is randomly selected n Randomly modifying the parameters of partial dimensionality to obtain a variant individual called parasitic vector and marked as X pv . Then randomly selecting an organism X from the ecosystem m (n ≠ m) as X pv The "host" of (1). Fitness values for the "parasitic vector" and the "host" are calculated and compared. If the fitness value of the "parasitic vector" is better, organism X m Will be substituted, otherwise X m Will be immune, continue to survive and remain in the population.
2.4 recombination stage
In an ecosystem, it is considered that a new organism produced by the propagation of the organism has a gene recombination event. Random selection of X m The parameter of partial latitude in (1) is used to replace X n Obtaining a new candidate solution by using the parameters of the same latitude
It is considered as a new organism
And gene recombination has occurred. According to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration.
2.5 mutation stage
In the ecosystem, the existence of genetic variation in new organisms generated by the propagation of organisms is considered. Original organism X n Due to the reception of organism X m And X k (k ≠ m) novel organisms that cause reproduction after long-term influence
The gene mutation was generated and represented by formula (6). According to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration.
F∈[0,1] (7)
In the formula, N, m, k belongs to {1,2, …, N } and N ≠ m ≠ k. F is a variation factor and is usually between 0.3 and 0.7. The size of the variation factor controls the magnitude of the variation of the new organism. The larger the value, the larger the magnitude of the new organism variation; conversely, the smaller the variation amplitude.
The specific process of the step 3 is as follows: the method comprises an objective function and an optimization variable of the island/grid-connected MG energy scheduling optimization and a power balance constraint of the island/grid-connected MG in the energy scheduling optimization. The dynamic switching model of the MG between an island mode and a grid-connected mode is considered by the island/grid-connected MG energy scheduling optimization model. The aim of the island/grid-connected MG energy scheduling optimization is to minimize the total operation cost, namely to maximize the economic benefit. Therefore, the objective function of the island/grid-connected MG energy scheduling optimization is defined as follows
τ t ∈{0,1} (9)
In the formula, C MG The method is the total operation cost of the island/grid-connected MG energy scheduling optimization. T is the total energy scheduling time.
And
the operating costs of CDG and BESS, respectively.
The representation of abandoning the RES cost means economic loss caused by the need of abandoning part of RES when island MG power is excessive.
The unloading cost is the economic loss caused by unloading part of Load when the island MG power is insufficient.
And
the electricity purchasing cost and the electricity selling cost from the grid-connected MG to the DN are respectively. Tau is t Is the status information of the PCC. When tau is t When the total running cost does not account for the cost of giving up the RES and the unloading cost, the PCC is in a closed state, the MG is connected with the DN at the moment, and the MG is in a grid-connected mode; when tau is t When the total operating cost does not account for the electricity purchasing and selling costs, the PCC is in a disconnected state, the MG is disconnected from the DN at the moment, and the MG is in an island mode.
The optimization variables of the island/grid-connected MG in the ISOS include the output power of the CDG, the charge and discharge power of the BESS, the number of RES waivers, the number of unloads, the number of purchases and sales of MG to DN, and the state change of the PCC. Therefore, the optimization variables of the island/grid-connected MG are defined as follows
In the formula, X n Is an optimization variable of the island/grid-connected MG in the ISOS. P t CDG Is the output power of the CDG. P t B+ And P t B- Charging and discharging of BESS, respectivelyElectrical power. P is t abRES And P t unLoad Respectively the number of abandoned RES and the number of unloads for an island MG. P is t Buy And P t Sell The electricity purchasing quantity and the electricity selling quantity from the grid-connected MG to the DN are respectively.
CDG, BESS, RES, Load, the amount of RES abandoned, the amount of unloading, the amount of electricity purchased and the amount of electricity sold need to meet the constraint conditions in the energy scheduling optimization. In addition, the islanding/grid-connected MG also needs to satisfy the power balance constraint in the energy scheduling optimization, as shown in formula (11).
P t Load ＝P t RES +P t CDG +(P t B- -P t B+ )+τ t ·(P t Buy -P t Sell )-(1-τ t )·(P t abRES -P t unLoad ) (11)
In the formula, P t Load And P t RES Respectively, the user load of the island/grid-connected MG and the renewable energy.
The specific process of the step 4 is as follows: the solution steps of the ISOS in performing optimization in the island/grid-connected MG energy scheduling are described. Fig. 2 is an ISOS-based ISOS island/grid-connected MG energy scheduling optimization flowchart. The specific flow of the algorithm is as follows:
step 4.1: inputting data of RES, Load and time-of-use electricity price of an island/grid-connected MG at the time T of 1,2, …;
step 4.2: ecosystem initialization of the ISOS: setting a population size N, a variable number D, a maximum iteration number G, a variation factor F and total energy scheduling time T;
step 4.3: initializing the population meeting the corresponding constraint conditions: POP ═ X 1 ,X 2 ,…,X N )；
Step 4.4: let the iteration number be gen 1;
step 4.5: calculating an objective function, namely a fitness value, of the island/grid-connected MG energy scheduling optimization according to the formula (8);
step 4.6: sorting the N fitness values from large to small and determiningOptimum individual X corresponding to minimum fitness value best ；
Step 4.7: let n equal to 1;
step 4.8: a mutualistic symbiosis phase is performed. Random selection of two individuals X n And X m Generating novel individuals according to formulas (1) and (2)
And
if the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.9: and executing a partial interest symbiosis stage. Generation of novel entities according to equation (5)
If the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.10: the parasitic phase is performed. Random selection of X n Randomly modifying the parameters of partial dimensionality to obtain a parasitic vector X pv . Then random individual X m (n ≠ m) as X pv The "host" of (1). If the fitness value of the "parasitic vector" is better, then the individual X m Will be X pv And (4) substitution.
Step 4.11: a recombination phase is performed. Random selection of X m Partial latitude parameter replacement X in (1) n Obtaining a new individual by using parameters of the same latitude
If the fitness value of the new individual is superior to that of the original individual X n The original individual is updated to a new individual according to the fitness value;
step 4.12: a mutation phase is performed. Generation of novel entities according to formula (6)
If the fitness value of the new individual is better than that of the original individualIf so, updating the original individual into a new individual;
step 4.13: let n be n + 1;
step 4.14: determine if N is greater than N? If not, skipping to step 4.8; if yes, executing step 4.15;
step 4.15: making iteration number gen as gen + 1;
step 4.16: determine if iteration number gen is greater than maximum iteration number G? If not, jumping to the step 4.6; if yes, executing step 4.17;
step 4.17: outputting the optimal individual X best And the algorithm ends.
FIG. 3 is a simulation result diagram of an ISOS and SOS based island/grid-connected MG energy scheduling optimization case of the present invention. It was simulated 50 times on MATLAB using ISOS and SOS, respectively, for a certain case. As can be seen from fig. 3, most of the results of ISOS-based ISOS/grid-connected MG energy scheduling optimization are better than the results of SOS-based ISOS-based isb/grid-connected MG energy scheduling optimization; and the result of ISOS-based island/grid-connected MG energy scheduling is more stable. Therefore, compared with the SOS, the stability and the economic benefit of the ISOS-based island/grid-connected MG energy scheduling optimization result are higher.
Claims (5)
1. A micro-grid energy scheduling optimization method based on improved symbiont search is characterized by comprising the following steps:
step 1, designing an island/grid-connected microgrid structure based on a multi-agent system: the structure of the system comprises renewable energy sources, storage battery diesel engines, loads, public connection points and a power distribution network; designing the connection relation among a micro-grid, renewable energy sources, storage batteries, a diesel engine, loads, public connection points, a power distribution network and an intelligent agent; and specifically illustrating the working relationship between the agents;
step 2, establishing a mathematical model for improving a symbiont search algorithm: the method comprises five stages of mutualism symbiosis, partial mutualism symbiosis, parasitism, recombination and variation;
step 3, establishing a mathematical model for scheduling and optimizing the energy of the island/grid-connected micro-grid: the method comprises an objective function, an optimization variable and a power balance constraint condition of the island/grid-connected micro-grid energy scheduling optimization;
step 4, designing an island/grid-connected micro-grid energy scheduling optimization process based on an improved symbiotic organism search algorithm: the method comprises the optimization steps of inputting data, improving ecological system initialization of a symbiotic organism search algorithm, initializing population, setting iteration times, calculating a fitness value, determining an optimal individual, executing a mutualism symbiosis stage, a partial mutualism symbiosis stage, a parasitic stage, a recombination stage, a variation stage, judging the iteration times and outputting the optimal individual;
the islanding/grid-connected microgrid refers to: the micro-grid can work in an island mode or a grid-connected mode, and the micro-grid realizes dynamic switching of the micro-grid in the island mode and the grid-connected mode by using a public connection point intelligent agent to control the on-off state of the public connection point.
2. The microgrid energy scheduling optimization method based on improved symbiotic search is characterized in that the specific process of designing the island/grid-connected microgrid structure based on the multi-agent system in the step 1 is as follows:
the island/grid-connected micro-grid structure based on the multi-agent system comprises renewable energy sources, a storage battery, a diesel engine, a load, a public connection point between the micro-grid and a power distribution network and the power distribution network, wherein the renewable energy sources, the storage battery, the diesel engine and the load are connected with one another through power transmission lines, and the power distribution network is connected with the renewable energy sources, the storage battery, the diesel engine and the load through the public connection point between the micro-grid and the power distribution network; the renewable energy source, the storage battery, the diesel engine, the load, a public connection point between the microgrid and the power distribution network are respectively connected with corresponding intelligent bodies, wherein the renewable energy source, the storage battery, the diesel engine, the load, the public connection point between the microgrid and the power distribution network and the intelligent bodies of the power distribution network are connected with the intelligent bodies of the microgrid; the intelligent agent connected with the renewable energy source is used for collecting power output information of the renewable energy source and sending an instruction whether the renewable energy source needs to be abandoned to the renewable energy source; the intelligent agent connected with the storage battery is used for collecting the charge state information of the storage battery and sending a charging/discharging power instruction to the storage battery; the intelligent agent connected with the diesel engine is used for collecting the upper and lower limit information of the output power of the diesel engine and sending an output power instruction to the diesel engine; the intelligent agent connected with the load is used for collecting the load value at each moment and sending an instruction whether to unload the load or not to the load; the intelligent agent connected with the public connection point between the micro-grid and the power distribution network is used for collecting the on-off state information of the public connection point between the micro-grid and the power distribution network and sending a closing or opening instruction to the public connection point between the micro-grid and the power distribution network; the intelligent agent connected with the power distribution network is used for collecting the power price information of the power distribution network and sending whether power selling is needed or not to the power distribution network; the micro-grid intelligent body is used for energy scheduling optimization; the intelligent agents connected with the renewable energy sources, the storage battery, the diesel engine, the load, a public connection point between the micro-grid and the power distribution network send all collected data information to the micro-grid intelligent agents; the energy scheduling instruction obtained by optimizing the micro-grid intelligent agent is sent to the intelligent agent connected with the renewable energy source, the storage battery, the diesel engine, the load, the public connection point between the micro-grid and the power distribution network; the island/grid-connected micro-grid structure based on the multi-agent system considers that the micro-grid is dynamically switched and operated between an island mode and a grid-connected mode through controlling a public connection point between the micro-grid and a power distribution network by an agent, and the improvement of the economic benefit of the micro-grid is facilitated.
3. The method according to claim 1, wherein the step 2 of establishing a mathematical model of the improved symbiont search algorithm comprises establishing mathematical models of a mutual benefit stage, a partial benefit symbiosis stage, a parasitic stage, a recombination stage and a variation stage, and comprises the following specific processes:
2.1 mutualistic symbiotic phase
The purpose of the mutualism symbiosis stage is to increase the mutual survival advantage of the two organisms in the ecosystem; new candidate solutions
And
is based on the organism X n And X m The reciprocal mutual profit generation is represented by formula (1) and formula (2); according to this rule, organism X n And X m The updating is performed only if their new fitness is better than the fitness of the previous iteration:
B 1 ,B 2 ∈{1,2} (4)
wherein N, m belongs to {1,2, …, N } (N is the population size), N ≠ m, and rand (0,1) is a random value between 0 and 1; x best Is the optimal individual for the current iteration, M v Is a "mutual advantage vector" of the characteristics of a relationship between two organisms, B 1 And B 2 Is a random number in the set {1,2} respectively, and represents the mutual income factor of the mutualistic symbiotic organisms;
2.2 paragenetic symbiosis
New candidate solutions
According to organism X n And X m The paragenetic symbiosis of (a) is represented by formula (5); according to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration:
wherein rand (-1,1) is a random number between-1 and 1;
2.3 parasitic
In improving symbiont search algorithm, X is randomly selected n Randomly modifying the parameters of partial dimensionality to obtain a variant individual called parasitic vector denoted as X pv (ii) a Then randomly selecting an organism X from the ecological system m (n ≠ m) as X pv The host of (1); calculating and comparing the fitness value of the parasitic vector and the fitness value of the host, and if the fitness value of the parasitic vector is better, then the organism X m Will be replaced, otherwise X m Will be immune, continue to survive and remain in the population;
2.4 recombination stage
In an ecosystem, the existence of gene recombination in a new organism generated by organism reproduction is considered; random selection of X m The parameter of partial latitude in (1) is used to replace X n Obtaining a new candidate solution by using the parameters of the same latitude
It is considered as a new organism
And gene recombination occurs; according to this rule, organism X n Performing an update only if its new fitness is better than the fitness of the previous iteration;
2.5 mutation stage
In an ecosystem, the existence of genetic variation in a new organism generated by organism reproduction is considered; original organism X n Due to the reception of organism X m And X k (k ≠ m) novel organisms that cause reproduction after long-term influence
Has undergone gene mutation(6) Representing; according to this rule, organism X n The update is only performed if its new fitness is better than the fitness of the previous iteration:
F∈[0,1] (7)
wherein N, m, k belongs to {1,2, …, N } and N is not equal to m not equal to k, F is a variation factor and usually takes a value of 0.3-0.7, the magnitude of the variation factor controls the magnitude of the new organism variation, and the larger the value is, the larger the magnitude of the new organism variation is; conversely, the smaller the variation amplitude.
4. The microgrid energy scheduling optimization method based on improved symbiotic search is characterized in that step 3 is used for establishing a mathematical model of island/grid-connected microgrid energy scheduling optimization, wherein the mathematical model comprises an objective function and an optimization variable of the island/grid-connected microgrid energy scheduling optimization and a power balance constraint of an island/grid-connected microgrid in the energy scheduling optimization, and the specific process comprises the following steps:
the mathematical model for the energy scheduling optimization of the island/grid-connected micro-grid needs to consider a dynamic switching model of the micro-grid between an island mode and a grid-connected mode; the aim of the energy scheduling optimization of the island/grid-connected micro-grid is to minimize the total operation cost, namely to maximize the economic benefit; therefore, the objective function of the energy scheduling optimization of the island/grid-connected micro grid is defined as follows
τ t ∈{0,1} (9)
In the formula, C MG Is the total operating cost of the energy scheduling optimization of the island/grid-connected micro-grid, T is the total energy scheduling time,
and
the operating costs of the diesel engine and the battery, respectively;
the cost of giving up renewable energy is represented, and the economic loss is caused by giving up part of renewable energy when the power of the island micro-grid is excessive;
the unloading cost is expressed, and is economic loss caused by the fact that partial load needs to be unloaded when the power of the island micro-grid is insufficient;
and
the electricity purchasing cost and the electricity selling cost from the grid-connected micro-grid to the power distribution network are respectively tau t The state information of the public connection point between the micro-grid and the distribution network; when tau is t When the total running cost does not account for the cost of giving up renewable energy sources and the unloading cost, a public connection point between the microgrid and the power distribution network is in a closed state, the microgrid is connected with the power distribution network at the moment, the microgrid is in a grid-connected mode; when tau is t When the total operation cost does not account for the electricity purchasing cost and the electricity selling cost, a public connection point between the micro-grid and the power distribution network is in a disconnection state, the micro-grid is disconnected with the power distribution network at the moment, the micro-grid is in an island mode, and the total operation cost does not account for the electricity purchasing cost and the electricity selling cost;
the optimized variables of the island/grid-connected microgrid in the improved symbiotic organism search algorithm comprise the output power of a diesel engine, the charging and discharging power of a storage battery, the number of abandoned renewable energy sources, the unloading number, the electricity purchasing and selling number of the microgrid to a power distribution network and the state change of a public connection point between the microgrid and the power distribution network, so the optimized variables of the island/grid-connected microgrid are defined as follows
In the formula, X n Is an optimized variable P of an island/grid-connected micro-grid in an improved symbiotic organism search algorithm t CDG Is the output power, P, of the diesel engine t B+ And P t B- Respectively the charging and discharging power of the accumulator, P t abRES And P t unLoad Respectively the abandoned renewable energy quantity and the unloading quantity, P, of the island micro-grid t Buy And P t Sell The electricity purchasing quantity and the electricity selling quantity from the grid-connected micro-grid to the power distribution network are respectively;
the method comprises the following steps that the constraint conditions of a diesel engine, a storage battery, renewable energy sources, loads, the quantity of abandoned renewable energy sources, the unloading quantity, the electricity purchasing quantity and the electricity selling quantity need to be met in energy scheduling optimization; in addition, the islanding/grid-connected microgrid also needs to satisfy power balance constraints in energy scheduling optimization, as shown in formula (11):
P t Load ＝P t RES +P t CDG +(P t B- -P t B+ )+τ t ·(P t Buy -P t Sell )-(1-τ t )·(P t abRES -P t unLoad ) (11)
in the formula, P t Load And P t RES Respectively, the user load and the renewable energy of the island/grid-connected micro-grid.
5. The microgrid energy scheduling optimization method based on improved symbiont search as claimed in claim 1, wherein the specific process of designing the island/grid-connected microgrid energy scheduling optimization flow based on the improved symbiont search algorithm in the step 4 is as follows:
the method comprises the step of solving an improved symbiont search algorithm when optimization is executed in the island/grid-connected microgrid energy scheduling, and the algorithm flow is as follows:
step 4.1: inputting data of renewable energy sources, loads and time-of-use electricity prices of an island/grid-connected micro-grid at the time T of 1,2, …;
step 4.2: ecosystem initialization for improving symbiont search algorithm: setting a population size N, a variable number D, a maximum iteration number G, a variation factor F and total energy scheduling time T;
step 4.3: initializing the population meeting the corresponding constraint conditions: POP ═ X 1 ,X 2 ,…,X N )；
Step 4.4: let the iteration number be gen 1;
step 4.5: calculating an objective function, namely a fitness value, of the energy scheduling optimization of the island/grid-connected micro-grid according to the formula (8);
step 4.6: the N fitness values are sequenced from large to small, and the corresponding optimal individual X with the minimum fitness value is determined best ；
Step 4.7: let n equal to 1;
step 4.8: and (3) executing a mutual interest symbiosis stage: random selection of two individuals X n And X m Generating a new individual according to the formula (1) and the formula (2)
And
if the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.9: and (3) executing a partial interest symbiosis stage: generating novel entities according to formula (5)
If the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.10: and (3) executing a parasitic stage: random selection of X n Randomly modifying the parameters of partial dimensionality to obtain a parasitic vector X pv (ii) a Then random individual X m (n ≠ m) as X pv The host of (1); if the fitness value of the parasitic vector is better, then the individual X m Will be X pv Substitution;
step 4.11: and (3) executing a recombination stage: random selection of X m Partial latitude parameter replacement X in (1) n Obtaining a new individual by using parameters of the same latitude
If the fitness value of the new individual is superior to that of the original individual X n The original individual is updated to a new individual according to the fitness value of the target;
step 4.12: performing a mutation phase: generation of novel entities according to formula (6)
If the fitness value of the new individual is superior to that of the original individual, the original individual is updated to the new individual;
step 4.13: let n be n + 1;
step 4.14: determine if N is greater than N? If not, skipping to step 4.8; if yes, executing step 4.15;
step 4.15: let iteration number gen be gen + 1;
step 4.16: determine if iteration number gen is greater than maximum iteration number G? If not, jumping to the step 4.6; if yes, executing step 4.17;
step 4.17: outputting the optimal individual X best And the algorithm ends.
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