CN107147105A - A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method - Google Patents
A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method Download PDFInfo
- Publication number
- CN107147105A CN107147105A CN201710236302.2A CN201710236302A CN107147105A CN 107147105 A CN107147105 A CN 107147105A CN 201710236302 A CN201710236302 A CN 201710236302A CN 107147105 A CN107147105 A CN 107147105A
- Authority
- CN
- China
- Prior art keywords
- distributed
- control
- intelligent agent
- hybrid
- constructing
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000010248 power generation Methods 0.000 claims description 36
- 230000008901 benefit Effects 0.000 claims description 18
- 238000011217 control strategy Methods 0.000 claims description 15
- 238000004146 energy storage Methods 0.000 claims description 15
- 230000007613 environmental effect Effects 0.000 claims description 5
- 238000011065 in-situ storage Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 15
- 230000010365 information processing Effects 0.000 abstract description 8
- 230000010354 integration Effects 0.000 abstract 1
- 239000003795 chemical substances by application Substances 0.000 description 82
- 230000005611 electricity Effects 0.000 description 26
- 230000006399 behavior Effects 0.000 description 12
- 230000004927 fusion Effects 0.000 description 9
- 230000003993 interaction Effects 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 7
- 230000002860 competitive effect Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 230000007774 longterm Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012887 quadratic function Methods 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
-
- H02J3/382—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Power Engineering (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method, specifically comprise the following steps：Step 1, build to collect and neutralize the distributed multistage intelligent body control framework being combined；Step 2, multiple space and time scales power market transaction strategy is proposed；Step 3, propose to mix control with distributed coordination by multistage intelligent body decision and the Multiple Time Scales hybrid optimization performed；Its Optimized-control Technique proposed also can switch and adjust optimization according to the packet loss of information system, network topology, transmission time lag and information processing capacity etc. and coordinate control structure and strategy in which assess information intelligent, realize that the security under information physical system depth integration environment, economy and the collaboration of dynamic quality multiple target become excellent.
Description
Technical Field
The invention belongs to the field of microgrid optimization control under the background of an energy internet, and particularly relates to a multi-space-time scale hybrid optimization and distributed coordination hybrid control method.
Background
The micro-grid is used as a main carrier for converting a traditional power grid into a smart grid and even an energy internet, and has the capabilities of accepting renewable energy power generation, configuring multi-energy power generation and managing power consumption of a demand side. The micro-grid comprises various distributed power generation, energy storage devices, loads and the like, the power supply, storage and utilization units not only have complex and various continuous dynamic behaviors, but also have interactive and mutually coordinated multi-mode switching behaviors, for example, the operation mode of the renewable energy power generation unit is limited by the random start and stop of natural conditions, the conversion of the charging and discharging operation modes of the energy storage device is often caused, meanwhile, the switching behaviors of large-amplitude switching adjustment of other hot standby power generation units and the switching operation of cold standby power generation units can be caused, even the load shedding and the like on the demand side can be caused, and therefore the micro-grid is a heterogeneous and uncertain multi-unit hybrid system. In order to greatly improve the consumption capacity of renewable energy power generation by a micro-grid so as to promote the conversion from the traditional power grid to a smart power grid, the following problems must be solved from the management and control angles of the micro-grid: (1) the multi-energy power generation is optimally scheduled, the space-time complementarity of the multi-energy power generation is fully utilized to make up for the defects of randomness, volatility and the like of the power generation of a single renewable energy source, and the utilization efficiency and the power supply reliability of the renewable energy source power generation are greatly improved; (2) the method comprises the steps of cooperatively controlling a power supply side and a demand side, namely changing the concept of only scheduling the power supply side in the prior art, introducing a demand side response mechanism while optimally scheduling the power supply side in a multi-time scale, and constructing a friendly and interactive cooperative mixed scheduling system of the power supply side and the demand side; (3) the market scheduling range is expanded, namely the micro-grid can be connected to a large power grid, and multi-space-scale market transaction can be carried out between the micro-grid and other remote micro-grids and a load demand side, renewable energy power generation is cooperatively consumed under the environment of friendly interaction between source-grid-load, and the load of the large power grid on power scheduling is reduced as much as possible; (4) an optimal control strategy of multi-target collaborative optimization under the condition of information physical deep fusion is explored by means of a brand-new control concept based on a communication technology, and therefore the micro-grid has the capacity of efficiently, economically, safely and excellently absorbing large-scale renewable energy. In a word, the spanning access of large-scale renewable energy sources endows the micro-grid with a new mission and form, and changes the composition structure, the operation mode, the behavior characteristics and the like of the micro-grid, so that the multi-space-time scale hybrid optimization and distributed coordination control technology under the deep fusion of information physics is explored and solved by a brand-new control concept from the mission of the micro-grid under the background of a smart grid and an energy internet.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-space-time scale hybrid optimization and distributed coordination hybrid control method aiming at the defects of the background technology, and the provided optimization control technology can also intelligently switch, adjust and optimize and coordinate control structures and strategies according to the evaluation information of the packet loss rate, network topology, transmission time delay, information processing capacity and the like of an information system, thereby realizing the multi-target collaborative optimization trend of safety, economy and dynamic quality under the deep fusion environment of an information physical system.
The invention adopts the following technical scheme to solve the technical problems
A multi-space-time scale hybrid optimization and distributed coordination hybrid control method specifically comprises the following steps:
step 1, constructing a centralized and distributed combined multi-stage intelligent agent control framework;
step 2, providing a multi-space-time scale electric power market trading strategy;
and 3, providing multi-time scale hybrid optimization and distributed coordination hybrid control decided and executed by the multi-level intelligent agent.
As a further preferable scheme of the multi-spatio-temporal scale hybrid optimization and distributed coordination hybrid control method of the present invention, the step 1 specifically comprises the following steps:
step 1.1, constructing a primary market trading agent between micro-grids distributed in different areas and a load demand side;
step 1.2, constructing a centralized secondary energy management intelligent agent in each micro-grid system;
step 1.3, constructing a centralized or multiple regional distributed coordination three-level coordination switching control intelligent bodies in each micro-grid system;
and 1.4, correspondingly constructing a distributed unit control intelligent agent and a distributed coordination secondary control intelligent agent based on an information network system in each distributed power generation, energy storage and local load in the microgrid, and combining the distributed unit control intelligent agent and the distributed coordination secondary control intelligent agent to be called a four-level dynamic regulation intelligent agent.
As a further preferable scheme of the multi-spatio-temporal scale hybrid optimization and distributed coordination hybrid control method of the present invention, the step 2 specifically comprises the following steps:
step 2.1, constructing two-stage time scale mathematical models of short-term and ultra-short-term transactions;
step 2.2, constructing a two-stage time scale benefit function;
step 2.3, solving a Nash equilibrium solution based on the cooperative game mode and the non-cooperative game mode;
and 2.4, determining the unique optimal solution of the Nash equilibrium according to the clearing price maximization principle.
As a further preferable scheme of the multi-spatio-temporal scale hybrid optimization and distributed coordination hybrid control method of the present invention, the step 3 specifically comprises the following steps:
step 3.1, constructing a hybrid model of each distributed power generation, energy storage and load unit;
step 3.2, coordinating, switching and controlling a mode coordinating, switching and controlling strategy in the intelligent agent to ensure the safety performance of the collaborative autonomous system;
step 3.3, the energy optimization management strategy is switched under the multi-mode in the EMS intelligent agent so as to ensure that the economic and environmental protection benefits of the system are maximized;
and 3.4, the unit controls a distributed coordination secondary control strategy under multi-mode switching among the intelligent agents and performs in-situ distributed control to obtain good dynamic quality.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) a multi-agent based control scheme with flexibility, expandability, and compatibility is presented. Based on the multi-agent technology, each unit function agent respectively utilizes real-time dynamic information in different ranges to construct management and control strategies in different theoretical scopes, executes management and control tasks in different levels, meets different time scale requirements, and effectively achieves the multi-target collaborative optimization of the whole system through the interaction of the multi-agent. In addition, the multi-agent system can flexibly adjust and configure the unit function agents in real time according to the change of the future power grid composition structure and the plug and play of the distributed micro power supply, and can also build or cancel the interactive behavior with the agent platforms of other power grids at any time, so that the control scheme has strong flexibility, expansibility and compatibility.
(2) A control scheme for information system and physical depth fusion is proposed. The coupling relation between the information system and the physical system is fully considered, and the control strategy of safety, economy and dynamic quality multi-target collaborative optimization is realized under the deep fusion environment of the information physical system according to the evaluation information intelligent compensation, adjustment optimization and coordination control strategy of the packet loss rate, network topology, transmission time lag, information processing capacity and the like of the information system.
(3) The hybrid control strategy of the multi-spatio-temporal scale hybrid optimization and the distributed coordination is provided, which integrates the coordination and the coordination between the multi-spatio-temporal scale hybrid optimization and the distributed coordination between the multi-spatio-temporal scale electric power market transaction → the safety evaluation of the information physical system based on the big data analysis → the coordination and the switching control of the operation modes based on the event triggering of the information physical system → the multi-spatio-temporal scale hybrid optimization scheduling → the networked consistency secondary control and the in-situ dispersion dynamic regulation.
Drawings
FIG. 1 is a schematic diagram of the present invention for building a centralized and distributed combined multi-level agent control architecture;
FIG. 2 is a schematic diagram of a multi-spatio-temporal scale electric power market trading strategy of the present invention;
FIG. 3 is a schematic diagram of multi-time scale hybrid optimization and distributed coordinated hybrid control as determined and performed by multiple levels of agents in accordance with the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
in order to achieve the above object, the present invention studies the following:
1. and constructing a centralized and distributed combined multi-stage intelligent agent control architecture. As shown in FIG. 1, the multi-agent system is designed with four stages: the primary market trading agent is responsible for managing and executing electric market trading among a large power grid, a micro power grid group and a load demand side so as to maximize the power supply and utilization benefits of the whole multi-energy system; the secondary energy optimization scheduling EMS intelligent agent is responsible for managing and executing multi-time scale hybrid optimization scheduling in the microgrid so as to maximize low-carbon economic benefit of the microgrid; the three-level coordination switching control intelligent agent executes coordination switching control of the micro-grid operation mode in a centralized or regional distributed coordination mode so as to realize flexible recombination and coordination switching of the operation mode under large disturbance and ensure the safety of micro-grid operation; the four-level multi-unit agent is responsible for executing distributed coordination secondary control and local control among the distributed power generation units so as to ensure the dynamic quality of the micro-grid power supply. The four-level multi-agent is cooperated and matched with each other in a centralized or distributed mode, the influence of evaluation indexes of the information system on the network system is fully considered under the deep fusion environment of the information physical system, and optimization and control strategies consisting of continuous or discrete behaviors in different space ranges and different time scales are determined and executed, namely, the mixed optimization and the distributed coordination hybrid control of multiple space-time scales under the deep fusion of the information physical system, so that the safe, efficient, economic and high-quality multi-target cooperative optimization of each unit body and the whole system is realized.
2. And providing a multi-space-time scale electric power market trading strategy. As shown in fig. 2, each microgrid may determine its power generation plan according to multi-time scale power generation prediction and schedulable power generation amount analysis, and the load demand side may determine its power demand based on multi-scale load prediction and schedulable power consumption analysis; then according to the objective wish of supply and Demand of the System, the System can be used as an electricity vendor or an electricity buyer to participate in the electric power market transaction, and reports the bidding sale, the electricity purchasing quantity and the electricity price to a market management center, namely a primary market transaction intelligent body, through an energy optimization management (EMS) intelligent body or a Demand side Response (DR) intelligent body of the System; and finally, the market trading intelligent agent determines the bid amount and the market unified price-giving of each micro-grid and the load demand side in multiple time scales, namely a multi-time-space scale market trading strategy, and sends the multi-time-scale market trading strategy to each micro-grid EMS or the load demand side intelligent agent, so that multi-time scale optimized scheduling is carried out according to the multi-time-scale market trading strategy.
3. And multi-time scale hybrid optimization and distributed coordination hybrid control decided and executed by a multi-level agent are provided. As shown in fig. 3, this item is the core and focus of the present invention. The method comprises the following steps: (1) based on the safety pre-estimation index of the information system and the pre-estimation and judgment of the unsafe event configuration of the physical system under big data analysis, when the information processing capacity and the transmission time lag of the information system are judged to exceed the allowable upper limit, in order to ensure the real-time performance of switching control, the three-level coordination switching control intelligent body determines and executes the micro-grid operation mode coordination switching control in a region distributed coordination mode; otherwise, determining and executing the coordination switching control in a centralized mode; when an unsafe event is judged to occur in the physical system of the microgrid in advance, determining an operation mode switching control strategy triggered by the event according to the configuration of the unsafe event, sending a control command by a three-level coordination switching control intelligent body, and transmitting the control command to a distributed generation, energy storage and internal load unit intelligent body through an information system to execute coordination switching among operation modes of each unit; (2) and each unit control intelligent agent transmits the switched operation mode information to the energy management intelligent agent through the information system, the energy management intelligent agent determines a microgrid multi-time scale electric power market trading strategy under a new operation mode according to different periods of multi-time scale trading, and the optimal power distribution value is issued to each distributed unit system through the information system. In addition, in order to comprehensively consider the mutual influence of the information physical fusion system, when the packet loss rate and the time lag of the information system exceed the specified allowable upper limit, a compensation mechanism is added in the real-time energy optimization scheduling, so that the influence of the information transmission problem on the real-time optimization scheduling of the physical system is reduced. (3) Finally, each unit multi-agent carries out local and secondary dynamic performance adjustment of frequency and voltage through distributed coordination secondary control and local distributed control based on networking according to the power distribution value. In addition, when distributed coordination secondary control is designed, the influence of the network topology and the transmission time lag of an information system is considered, and a networking consistency control strategy based on switching topology and having time lag robustness is provided. Therefore, the coordinated and matched multi-time scale optimization scheduling and distributed coordination hybrid control among the two-layer to four-layer agents from the safety performance prediction under big data → the switching control based on discrete event triggering → the energy optimization management → the continuous dynamic regulation are realized. Because the electric power market transaction realized in the first-level intelligent agent also belongs to the problem of optimized scheduling, compared with the optimized scheduling of the second-level EMS intelligent agent, the space dimensionality is different, namely the first-level intelligent agent performs optimized scheduling among microgrid groups, and the second-level intelligent agent performs distributed power generation in the microgrid. Therefore, the one-level to four-level multi-agent system provided by the invention executes multi-space-time scale hybrid optimization scheduling and distributed coordination hybrid control.
Embodiment of content 1: a physical system of a microgrid is taken as a research object, an information system is taken as a support, a four-level multi-agent control architecture is constructed as shown in figure 1, and the purpose is to ensure that large-scale renewable energy sources in the microgrid can be efficiently, safely, economically and excellently consumed and utilized in a coordinated interaction mode of centralized type and distributed type multi-agent. The four-level multi-agent structure and function is as follows:
(1) and a primary market trading agent is constructed between the micro-grid distributed in different areas and the load demand side. And determining and executing a multi-time-scale market trading strategy according to the reported multi-time-scale power supply and power utilization requirements of each micro-grid energy management intelligent agent and the load demand side management intelligent agent so as to ensure the maximization of power supply and power utilization benefits. The BDI intelligent agent is structurally designed to be composed of belief, desire and intention functional modules, the belief module filters and screens standardized knowledge information from an information system, and based on useful standardized knowledge information of the belief, the belief module controls the desire module and the intention module to intelligently decide strategies according to the intelligent agent.
(2) And a centralized secondary energy management intelligent body is constructed in each micro-grid system. The energy optimization management system is responsible for determining and executing an energy optimization management strategy, and realizes optimal power allocation of the schedulable distributed power generation and energy storage units in the micro-grid system on the premise of ensuring market trading power and self system load power supply so as to ensure that economic and environment-friendly benefits of the micro-grid are maximized. The BDI intelligent body is also structurally designed to be composed of belief, desire and intention functional modules, the belief module filters and screens standardized knowledge information from an information system, and based on useful standardized knowledge information of the belief, the belief module controls the desire and intention modules to intelligently decide an optimization strategy.
(3) And a centralized or multi-region distributed coordination three-level coordination switching control intelligent agent is constructed in each micro-grid system. Firstly, determining whether a three-level agent adopts a centralized or distributed coordination mode to execute coordination switching control by pre-estimating information processing capacity and transmission time lag of an information system; and then, based on the safety pre-estimation and unsafe event configuration judgment of the physical system of the micro-grid, determining and executing a coordinated switching control strategy triggered by an event, so that when the micro-grid is threatened by the safety, the operation modes of unit systems such as distributed generation, energy storage, local load and the like can be coordinated and switched, and the safety performance of the system can be ensured. The structure is also designed as a BDI agent consisting of 'belief, desire and intention' functional modules.
(4) A distributed unit control intelligent agent and a distributed coordination secondary control intelligent agent based on an information network system are correspondingly constructed in each distributed power generation, energy storage and local load in a microgrid, and the distributed unit control intelligent agent and the distributed coordination secondary control intelligent agent are called four-level dynamic regulation intelligent agents in a combined mode. And determining and executing secondary control and decentralized local dynamic control to realize dynamic adjustment of power generation or power utilization power so as to ensure the dynamic quality of the output power of the microgrid. The four-level unit intelligent agent is designed into a hybrid BDI intelligent agent with a reaction layer and a consultation layer, wherein the reaction layer comprises a sensing, identifying and executing module and can quickly react to the change of the operating environment, so that the adaptability of the micro-grid to the environmental change is ensured; the consultation layer comprises a function module of 'belief, desire and intention', and can process the state of the distributed power generation unit into knowledge information so as to intelligently decide and execute the unit operation mode switching and dynamic control strategy.
According to the multi-agent control architecture constructed by the invention, a master-slave interaction mode is adopted between longitudinal agents, and because safety, economy, high efficiency and high quality multi-target comparison are the primary guaranteed targets, the three-level agents have the highest priority, when an unsafe event is estimated to occur, the three-level agents send a switching control instruction to the four-level agents to execute switching, the switched modal information is sent to the two-level energy management agents, an energy optimization management strategy under a new modal configuration is determined and executed, then an optimal power assignment value is sent to each unit agent of the four-level agents, and distributed coordination secondary control and distributed local control are executed; and the horizontal peer multi-unit agents are in a non-master-slave interaction mode, namely, the horizontal peer multi-unit agents have equal interaction rights.
Embodiment of content 2: the multi-space scale electric power market transaction means that the micro-grid can be selected to be independent or alliance with other micro-grids in different regional spaces for bidding; the multi-time scale means that market trading is divided into medium-long term (year, season and month), short term (day) and ultra-short term (hour or minute) market trading according to the time scale, and the medium-long term market trading is usually contract market, i.e. buyer and seller sign medium-long term electricity purchase contract through negotiation, so the short term and ultra-short term market trading is mainly researched here. The invention selects two time scale bidding modes of short-term subsection bidding and ultra-short-term continuous bidding, and a multi-space scale Nash balanced game mode combining independent and cooperative games, thereby not only comprising bidding strategies of bidding price and bidding electric quantity, but also trading according to unified maximum market clearing price. The implementation scheme of the multi-spatio-temporal scale market trading strategy is as follows:
(1) and constructing a two-stage time scale mathematical model of short-term and ultra-short-term transactions. The market trading model based on the game theory mainly constructs the following fourIndividual elements 1) participants: each distributed microgrid and load demand side are considered market trading participants. During short-term (day-ahead) transaction, each participant regards the long-term purchase contract as long-term load prediction (electricity sales amount is regarded as positive load, and electricity purchase amount is regarded as negative load), and the sum of the long-term purchase contract and the predicted load values of the internal short-term segments is regarded as total load predicted value; then, the EMS intelligent agent of each participant determines the competitive bidding electric quantity and the trading power price of each short-term section according to the difference value between the predicted value of the electric energy generation of each short-term section and the predicted value of the total load, and reports the competitive bidding electric quantity and the trading power price to the market trading intelligent agent; meanwhile, the DR intelligent agent on the load demand side reports the bidding purchase electric quantity and the trading price of each short-term section to the market trading intelligent agent according to the total load predicted value of each short-term section and the response adjustable value of the load demand side; if the competitive bidding electric quantity is positive, the participant is the electricity selling participant, otherwise, the participant is the electricity purchasing participant. During the ultra-short term (daily) transaction, each participant regards the sum of the long-term purchase contract and the short-term bid amount in each section and the internal ultra-short term load predicted value thereof as a total load predicted value; then, the EMS intelligent agent of each participant determines the ultra-short competitive electric quantity and the trading electric price according to the difference value between the ultra-short generating capacity predicted value and the total load predicted value, and reports the ultra-short competitive electric quantity and the trading electric price to the market trading intelligent agent; meanwhile, the DR intelligent agent on the load demand side reports the ultra-short-term bidding purchasing electric quantity and the trading electricity price to the market trading intelligent agent according to the ultra-short-term total load predicted value and in combination with the response adjustable value of the load demand side; and if the competitive bidding electric quantity is positive, the participant is the electricity selling participant, otherwise, the participant is the electricity purchasing participant. 2) Bidding strategy: the short-term transaction selects a segmented bidding strategy, and the ultra-short-term transaction is a continuous bidding strategy. The short-term segment bidding strategy of each participant is as follows:Qis∈[Pi min,Pi max]∈ S, wherein,the number of the participators is the number,for the sectional bidding period of the ith participant, SisStrategy for representing the s-th segmented bidding period of the ith participant, ζis，Andrespectively bidding the ith participant in the s-th sectional bidding period for the bidding price and the upper and lower limit values, Qis,Pi maxAnd Pi minAnd (4) bidding the electricity quantity and the upper limit and the lower limit of the electricity generation or the electricity utilization capacity for the ith participant in the s-th subsection bidding period. The ultra-short term bidding strategy is as follows:whereinAndrespectively representing the ultra-short-term bidding strategy, the bidding price and the bidding electric quantity of the ith participant. 3) The benefit function: this element will be discussed in the study scheme for constructing the benefit function. 4) Nash equilibrium conditions: this element will be discussed in the study scheme for solving the Nash equilibrium solution.
(2) And constructing a two-stage time scale benefit function. 1) Constructing a cost function of the ith power generation participant in the short-term s-th subsection bidding period to be expressed as follows:wherein a is0is,a1isAnd a2isThe coefficient is a coefficient, the coefficient of a quadratic function is related to the average fuel cost, initial investment, preferential policy, operation and maintenance cost and the like of the unit generated power of the micro-grid, and the coefficient is determined by adopting a curve fitting method and a parameter estimation method. 2) Constructing the ith Power Generation participant shortThe bidding function of each period is to be expressed as:wherein λisA benefit metric factor to be determined for the ith participant during the s-th staged trading period. 3) Constructing a short-term benefit function of each section of the ith power generation participant to be expressed as follows:
furthermore, it is possible to provide a liquid crystal display device,wherein gamma issFor the uniform trade of the market, PMGisAnd ζisRespectively, the middle scalar quantity and bidding price, mu, of the ith participant in the s-th segmented bidding periodisAnd (4) giving out the difference value between the clearing price and the bidding price for the market. 4) Constructing a short-term benefit function of each section of the load demand side:PLdsis the standard purchase of electricity quantity, P, in the load demand sideILjsIs j load shedding loads on the load demand side, β1sAnd β0sCompensating the price coefficient, C, for the power adjustment, respectivelyILjsIs the jth load shedding price per power loss. 5) Establishing short-term transaction constraint conditions: the bidding and winning electricity quantity is limited in the strategy space range of the participants, the winning electricity quantity is less than or equal to the bidding electricity quantity, the market clearing price is limited in the price range specified by the trading market, the medium and long term electricity selling and purchasing contract, the short term winning electricity quantity in each section and the internal load forecast are the predicted value of the electricity generation in each section in a short term, the load schedulable range and the interruption time constraint are achieved, and the like. (6) The construction of the ultra-short term benefit function is basically similar to that of the short term, and the difference is only two points: firstly, a continuous bidding strategy rather than a sectional bidding strategy is adopted; second, in the constraint condition, medium and long term electricity selling and purchasing contract, medium and short term electricity winning amount in each segment, ultra short term electricity winning amount in the short term, ultra short term internal load prediction and ultra short term electricity generationAnd (4) predicting the quantity.
(3) And solving a Nash equilibrium solution based on cooperative play and non-cooperative (independent) game modes. 1) The above-mentioned benefit objective function optimization problem (MPEC) belongs to the nonlinear optimization problem, which is to be converted into a Mixed Integer linearization Model (MILP) by Binary extension (Binary extension), for example, the bidding Binary extension mode can be expressed as:then it is determined that,by analogy, all non-linear terms in the benefit objective function can be converted into a mixed integer linearized form. 2) Nash equilibrium conditions: the nash equilibrium condition under each segment of the short-term independent game can be expressed as:wherein,nash equilibrium solution for all participants; the nash balance condition in a non-standalone game (e.g., participants 1-3 collaboratively play, and other participants play standalone games) can be expressed as: ultrashort term transactions are similar to those described above. 3) Under the Nash Equilibrium condition, the MPEC model which is subjected to binary extension transformation can be converted into a Nash Equilibrium Problem (EPEC) with Equilibrium constraint, and a plurality of groups of Nash Equilibrium solutions can be solved through the EPEC-MILP.
(4) And determining the only optimal solution of Nash equilibrium by the maximum clearing price principle. The scheme can be expressed asAnd (3) optimizing the problem, namely selecting a group of optimal solutions with the maximum market clearing value from a plurality of groups of Nash equilibrium solutions of cooperative/independent games in each section of the short-term and ultrashort-term trading, wherein the optimal solutions comprise the marked-purchase electric quantity and the maximum market unified clearing value in each section of the short-term of all participants under different spatial combinations, and the marked-purchase electric quantity and the market unified maximum clearing value in the ultrashort-term, namely the optimal solutions are the multi-space-time scale market trading strategy.
Embodiment of content 3: the implementation scheme of the multi-time scale hybrid optimization and distributed coordination hybrid control is as follows:
(1) and researching and constructing a hybrid model of each distributed power generation, energy storage and load unit. The modeling method based on differential hybrid Petri-net constructs a hybrid model of the unit system, and the model not only describes the operation modes of each unit and the logic switching relation between the modes, but also describes the continuous dynamic behavior characteristic under each operation mode, namely the hybrid behavior characteristic of the unit system is described. One purpose of constructing the model is to execute effective mode coordination switching according to the estimated unsafe event configuration and the logic switching relation among the unit system modes, namely to realize mode switching control under event triggering; the second purpose is to design continuous dynamic control according to the continuous dynamic behavior characteristics of each mode of the unit system so as to ensure the dynamic performance of the unit system under multi-mode switching, so that the research lays a model foundation for the design of subsequent mode coordination switching control and continuous dynamic control.
(2) And researching a mode coordination switching control strategy in the coordination switching control agent to ensure the safety performance of the cooperative autonomous system. 1) Based on information big data analysis, estimating the network transmission time lag and the information processing capacity of the information system by utilizing deep mining and information fusion technologies; meanwhile, the safety of the physical system of the micro-grid is estimated, and voltage and frequency safety estimation indexes are constructed; 2) based on the information system transmission time lag and information processing capability evaluation indexes, when the values of the information system transmission time lag and the information processing capability evaluation indexes are smaller than a specified upper limit value, the three-level intelligent agent executes coordination switching control in a centralized mode; and if at least one is larger than the specified upper limit value, adopting a distributed coordination mode by the three-level agents. Then, judging the configuration of unsafe trigger events based on the voltage and frequency safety pre-estimation indexes of the physical system of the micro-grid, wherein the unsafe trigger events comprise multi-point overhigh and overlow events and overhigh and overlow events of frequency, so that the configuration of the unsafe events can be an event form or a combination form of several events; 3) based on unsafe trigger event configuration and differential hybrid Petri-net models of all unit systems, a mode switching control strategy under event trigger is constructed: corresponding to each unsafe trigger event configuration, placing selectable mode switching combinations in a set according to the selection sequence according to the operation modes described by the unit hybrid models and the logic switching relation among the operation modes, which is called a mode switching configuration set under the unsafe events; and then constructing a Constraint Violation Function (Constraint vision Function) for describing the switching cost, and determining the optimal mode coordination switching strategy according to the sequence in the mode switching configuration set under the unsafe event and by taking the Violation Function as '0' or a minimization principle.
(3) And researching an energy optimization management strategy under multi-mode switching in the EMS intelligent agent to ensure that the economic and environmental benefits of the system are maximized. 1) Based on the operation modal information, the power generation capacity and the demand predicted value of all distributed power generation, energy storage and local load units, and according to a multi-time-space-scale market trading strategy, under the friendly interaction of a power generation end and a demand side, an economic and environment-friendly multi-target function and constraint conditions of multi-time-scale discrete and continuous behavior interaction under a multi-mode switching behavior are constructed. The economic objective function consists of operation cost, starting cost, maintenance cost and the like; the environmental protection objective function is constructed according to the pollution emission amount, the pollution emission gas type, the punishment cost and the like of the unit generating power. 2) And based on the evaluation indexes of the transmission time lag and the packet loss rate of the information system, adding a prediction compensation mechanism into the objective function when the transmission time lag and the packet loss rate of the information system exceed the specified upper limit value. The specific method is that each step of real-time rolling optimization is pre-pushed forward by a plurality of steps on a time scale, and when the transmission time lag and the packet loss rate of the information system exceed the specified upper limit values, the rolling optimization value calculated in real time is replaced by the prediction optimization value of the time lag corresponding to the time period. And when the transmission time lag and the packet loss rate of the information system are reduced to be lower than the specified upper limit value, the substitution is cancelled. 3) In order to improve the convergence rate and the generalization capability of the optimization algorithm, an improved particle swarm optimization method is adopted to obtain the optimal solution of the nonlinear multi-objective function under the multi-constraint condition: the optimal solution is the optimal reference power allocation of each distributed power generation and energy storage unit corresponding to each operation mode.
(4) The research unit controls a distributed coordination secondary control strategy under multi-mode switching between the intelligent agents and local distributed control to obtain good dynamic quality. 1) Based on a networked consistency control theory, the change of a network topological structure of an information system and information transmission time lag are considered, and distributed coordination secondary control is researched and designed to realize secondary adjustment of voltage and frequency. The secondary control consistency strategy is used as the outer loop power droop control input of bottom layer local distributed control; 2) designing a power controller based on the improved P-Q, f-V droop characteristic to enable power proportion sharing among distributed power generation to be achieved, and enabling voltage frequency to be consistent; 3) based on the dynamic behavior characteristics of distributed power generation, energy storage and local load units in each operation mode, a local on-site continuous controller is designed, namely a voltage and current dual-ring controller is designed through a power electronic control device of a rectifier/inverter interface and based on a multi-Lyapunov robust control method by utilizing local information aiming at the distributed power generation and energy storage units so as to ensure the frequency/voltage or active/reactive robust stability performance under multi-mode switching. 4) And aiming at controllable load units with different characteristics and different levels, emergency load management strategies such as graded load shedding, nonlinear tracking control based on load characteristics and the like are respectively designed.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A multi-space-time scale hybrid optimization and distributed coordination hybrid control method is characterized in that: the method specifically comprises the following steps:
step 1, constructing a centralized and distributed combined multi-stage intelligent agent control framework;
step 2, providing a multi-space-time scale electric power market trading strategy;
and 3, providing multi-time scale hybrid optimization and distributed coordination hybrid control decided and executed by the multi-level intelligent agent.
2. The method of claim 1, wherein the hybrid control method comprises the following steps: the step 1 specifically comprises the following steps:
step 1.1, constructing a primary market trading agent between micro-grids distributed in different areas and a load demand side;
step 1.2, constructing a centralized secondary energy management intelligent agent in each micro-grid system;
step 1.3, constructing a centralized or multiple regional distributed coordination three-level coordination switching control intelligent bodies in each micro-grid system;
and 1.4, correspondingly constructing a distributed unit control intelligent agent and a distributed coordination secondary control intelligent agent based on an information network system in each distributed power generation, energy storage and local load in the microgrid, and combining the distributed unit control intelligent agent and the distributed coordination secondary control intelligent agent to be called a four-level dynamic regulation intelligent agent.
3. The method of claim 1, wherein the hybrid control method comprises the following steps: the step 2 specifically comprises the following steps:
step 2.1, constructing two-stage time scale mathematical models of short-term and ultra-short-term transactions;
step 2.2, constructing a two-stage time scale benefit function;
step 2.3, solving a Nash equilibrium solution based on the cooperative game mode and the non-cooperative game mode;
and 2.4, determining the unique optimal solution of the Nash equilibrium according to the clearing price maximization principle.
4. The method of claim 1, wherein the hybrid control method comprises the following steps: the step 3 specifically comprises the following steps:
step 3.1, constructing a hybrid model of each distributed power generation, energy storage and load unit;
step 3.2, coordinating, switching and controlling a mode coordinating, switching and controlling strategy in the intelligent agent to ensure the safety performance of the collaborative autonomous system;
step 3.3, the energy optimization management strategy is switched under the multi-mode in the EMS intelligent agent so as to ensure that the economic and environmental protection benefits of the system are maximized;
and 3.4, the unit controls a distributed coordination secondary control strategy under multi-mode switching among the intelligent agents and performs in-situ distributed control to obtain good dynamic quality.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710236302.2A CN107147105A (en) | 2017-04-12 | 2017-04-12 | A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710236302.2A CN107147105A (en) | 2017-04-12 | 2017-04-12 | A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107147105A true CN107147105A (en) | 2017-09-08 |
Family
ID=59773574
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710236302.2A Pending CN107147105A (en) | 2017-04-12 | 2017-04-12 | A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107147105A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107887933A (en) * | 2017-11-17 | 2018-04-06 | 燕山大学 | A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method |
| CN109449985A (en) * | 2018-12-11 | 2019-03-08 | 燕山大学 | A kind of grid control method and system |
| CN110611333A (en) * | 2019-11-11 | 2019-12-24 | 燕山大学 | Island microgrid distributed coordination control method considering communication problem |
| CN111342446A (en) * | 2020-03-02 | 2020-06-26 | 国网天津市电力公司电力科学研究院 | Three-dimensional household intelligent energy-consumption ubiquitous scheduling control method and system |
| CN113435120A (en) * | 2021-06-30 | 2021-09-24 | 燕山大学 | Load running state and relay bandwidth cooperative optimization method based on temperature control load |
| CN113487207A (en) * | 2021-07-16 | 2021-10-08 | 重庆大学 | Multi-target energy management system in multi-energy community based on multi-agent system optimal user cluster |
| US11817707B2 (en) | 2018-12-19 | 2023-11-14 | King Fahd University Of Petroleum And Minerals | Smart meter system and method for managing demand response in a smart grid |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102810186A (en) * | 2012-08-01 | 2012-12-05 | 江苏省电力设计院 | Multi-time scale microgrid energy optimizing management system structure and method |
| CN103001225A (en) * | 2012-11-14 | 2013-03-27 | 合肥工业大学 | MAS-based (multi-agent system) multi-microgrid energy management system simulation method |
| US20140222887A1 (en) * | 2013-02-01 | 2014-08-07 | Introspective Power, Inc. | Generic distributed processing unit for multi-agent systems |
-
2017
- 2017-04-12 CN CN201710236302.2A patent/CN107147105A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102810186A (en) * | 2012-08-01 | 2012-12-05 | 江苏省电力设计院 | Multi-time scale microgrid energy optimizing management system structure and method |
| CN103001225A (en) * | 2012-11-14 | 2013-03-27 | 合肥工业大学 | MAS-based (multi-agent system) multi-microgrid energy management system simulation method |
| US20140222887A1 (en) * | 2013-02-01 | 2014-08-07 | Introspective Power, Inc. | Generic distributed processing unit for multi-agent systems |
Non-Patent Citations (2)
| Title |
|---|
| LI GUANGRU: "The Shared and Cooperated Application of WebGIS Based on MAS and Grid", 《2009 INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND MINING》 * |
| 贾星蓓，窦春霞，岳东: "基于多代理系统的微电网多尺度能量管理", 《电工技术学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107887933A (en) * | 2017-11-17 | 2018-04-06 | 燕山大学 | A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method |
| CN109449985A (en) * | 2018-12-11 | 2019-03-08 | 燕山大学 | A kind of grid control method and system |
| CN109449985B (en) * | 2018-12-11 | 2020-06-26 | 燕山大学 | Microgrid control method and system |
| US11817707B2 (en) | 2018-12-19 | 2023-11-14 | King Fahd University Of Petroleum And Minerals | Smart meter system and method for managing demand response in a smart grid |
| CN110611333A (en) * | 2019-11-11 | 2019-12-24 | 燕山大学 | Island microgrid distributed coordination control method considering communication problem |
| CN110611333B (en) * | 2019-11-11 | 2022-12-13 | 燕山大学 | Island microgrid distributed coordination control method considering communication problem |
| CN111342446A (en) * | 2020-03-02 | 2020-06-26 | 国网天津市电力公司电力科学研究院 | Three-dimensional household intelligent energy-consumption ubiquitous scheduling control method and system |
| CN111342446B (en) * | 2020-03-02 | 2023-10-27 | 国网天津市电力公司电力科学研究院 | Three-dimensional household intelligent energy ubiquitous dispatching control method and system |
| CN113435120A (en) * | 2021-06-30 | 2021-09-24 | 燕山大学 | Load running state and relay bandwidth cooperative optimization method based on temperature control load |
| CN113487207A (en) * | 2021-07-16 | 2021-10-08 | 重庆大学 | Multi-target energy management system in multi-energy community based on multi-agent system optimal user cluster |
| CN113487207B (en) * | 2021-07-16 | 2022-06-28 | 重庆大学 | Multi-target energy management system in multi-energy community based on multi-agent system optimal user cluster |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xiong et al. | Multi-agent based multi objective renewable energy management for diversified community power consumers | |
| Wang et al. | Research on economic optimization of microgrid cluster based on chaos sparrow search algorithm | |
| Jin et al. | Local flexibility markets: Literature review on concepts, models and clearing methods | |
| Ghadi et al. | A review on economic and technical operation of active distribution systems | |
| Gazijahani et al. | Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach | |
| Ahmad et al. | A review of microgrid energy management and control strategies | |
| CN107147105A (en) | A kind of multiple space and time scales hybrid optimization and distributed coordination mixed control method | |
| Xing et al. | Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community | |
| Nikmehr et al. | Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty | |
| Du et al. | Coordinated energy dispatch of autonomous microgrids with distributed MPC optimization | |
| EP3000161B1 (en) | Power supply network control system and method | |
| Bao et al. | A multi time-scale and multi energy-type coordinated microgrid scheduling solution—Part II: Optimization algorithm and case studies | |
| Moghaddam et al. | Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study | |
| Jin et al. | Scheduling distributed energy resources and smart buildings of a microgrid via multi‐time scale and model predictive control method | |
| Vera et al. | Two-stage stochastic optimization model for multi-microgrid planning | |
| Solanke et al. | Control and management of a multilevel electric vehicles infrastructure integrated with distributed resources: A comprehensive review | |
| Yin et al. | Hybrid metaheuristic multi-layer reinforcement learning approach for two-level energy management strategy framework of multi-microgrid systems | |
| Zhang | Optimal design and planning of energy microgrids | |
| Suchetha et al. | Optimization techniques for operation and control of microgrids–Review | |
| Huang et al. | Jointly optimizing microgrid configuration and energy consumption scheduling of smart homes | |
| Dong et al. | Application of optimal production control theory for home energy management in a micro grid | |
| Dagdougui et al. | Towards a concept of cooperating power network for energy management and control of microgrids | |
| Khojasteh et al. | A novel adaptive robust model for scheduling distributed energy resources in local electricity and flexibility markets | |
| Zadeh et al. | IoT-based stochastic EMS using multi-agent system for coordination of grid-connected multi-microgrids | |
| Si et al. | Cloud-edge-based we-market: Autonomous bidding and peer-to-peer energy sharing among prosumers |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170908 |
|
| WD01 | Invention patent application deemed withdrawn after publication |