Modeling And Algorithm Of Microgrid Systems Optimization Considering Demand Response

With the development of energy sources interconnection concept,research on microgrids(MGs)considering demand response and uncertainties is triggered by the increasing penetrations of renewable energy sources,active loads applications,and plug-in electric vehicles(PEVs).The coordination of energy “supply-demand” system and active loads is crucial to the improvement of system operation efficiency.In this dissertation,I aim at addressing the following challenges for the research modeling and algorithm of demand response in microgrid systems considering uncertainties.The coordination effect between supply and demand sides of microgrids will be significantly enhanced by employing these achieved researches,which can also provide theory and technology support for the applications of microgrid systems in real world.Specifically,the main research works and contributions include the following five parts:(1)A type of optimal and dynamic microgrid energy consumption scheduling framework is formulated to minimize the daily total operation cost,while fully considering the output forecast error of renewable energy sources(RESs),the consumption preference of users and the status of the energy storage system.In this framework,a unified appliance model is provided to group various types of appliances,which are connected to the system,into a physical model with the same attributes,and an internal comprehensive real-time pricing mechanism is developed based on the generalized total load to guide the electricity consumption behavior of the end-users and to also balance the total residential load.On this basis,a mixed integer programming(MIP)model for dynamic energy management optimization of the microgrid is optimized at each decision period and integrated into a model predictive control method to reduce the negative impacts of forecast errors of RESs.(2)A two-stage real time optimization strategy for microgrid is proposed.Different from most existing studies that focus on off-line demand side management(DSM)in microgrids while neglecting forecasting errors of uncertain renewable generations,this chapter studies on-line DSM.A two-stage real-time DSM(RDSM)method for a microgrid including different time scales,integrated with schedulable ability(SA)and uncertainties,is proposed.In the first stage,a model predictive control-based dynamic optimization is applied to minimize the operation cost and maintain the power balance considering the uncertainties imposed by both supply and demand sides in the MG system.In the second stage,the concept of novel SA is defined for response executors(REs)and also establish an SA evaluation system taking the real-time and history information of the REs into account.In doing so,a faster-time scale on-line power allocation among REs is carried out in the framework of dynamic optimization to further compensate for the uncertainties in real-time,based on the evaluated SA values of the responsive executors and the required compensation power.(3)A blockchain-based ADR(BADR)method for energy storage system in a microgrid is presented to do some related research on the beneficial application pattern of blockchain technology in energy field.The structure of the microgrid was described from the blockchain perspective.Based on the congestion price algorithm,the decentralized ADR method was developed,and the response executors can rely on this rule-based ADR method to response the system compensation needs independently.On this basis,the intelligent contracts among the response executors were established to ensure the efficient implementation of energy trading and profit distribution.Case studies based on a certain workspace grid-connected microgrid system demonstrate the rationality and effectiveness of the proposed approach.(4)An event-driven automatic demand response(EADR)framework is presented for online operation of residential microgrids(RMGs).Our framework involves comprehensive analysis of the SAs for residential energy resources(RERs),and the uncertainties of both sides are also considered.Specifically,we first construct an EADR architecture to minimize the total operation cost and maintain supply-demand balance,and the event analyses are provided.On these bases,SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way.Furthermore,to implement this framework in practice,we functionally decompose the overall EADR architecture into event manager,EADR server and a group of local controllers,and also design distributed online algorithms for each of them.The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics.(5)This chapter proposes a bilayer game-theoretic framework for the interactive energy management(IEM)of multi-microgrids(MMG)under the presence of uncertainties imposed by both supply and demand sides.The higher-layer of the framework manages the energy trading and the consumption behavior for each microgrid within a slow timescale,where its operation cost model is designed in terms of economic factors and users’ willingness.The lower-layer runs at higher frequency that relies on a model predictive control approach,and adjusts the microgrids operation to minimize the difference between the planed interactive strategies generated by the higher-layer and the real ones.In doing so,the supply and demand uncertainties as well as the outage events can be handled properly.As the internal price is adjusted with system net load,the power balances in MMG would be enhanced by minimizing operation cost.On this basis,the energy trading among microgrids can be achieved directly without an intermediary.I design a distributed interactive algorithm to implement the bilayer IEM framework by using the Nash equilibrium concept.Furthermore,the emergency situation is covered to enhance the resilience of the multi-microgrid system.