Research On Energy Sharing And Optimal Control Strategy Of Microgrid Cluster

The increase in energy demand and environmental problems due to the production of energy from fossil fuels introduces a new way of generating energy.Nearly all modern microgrids(MG)are concerned with dynamic frequency stability problems,particularly when operating in isolated mode,due to low inertia,stochastic nature of renewable energy sources(RESs),and abrupt load shifts.As a result,the potential number of renewable energy systems that can penetrate the MG is limited.The frequency stabilization problems must be resolved by adding extra inertial assistance to maximize the penetration of low-inertia sources into the MG.In this thesis hybrid ESS(HESS)[BESS+supercapacitor(SC)]is considered as a potential candidate for the enhancement of MG inertia.Compared with other energy storage technologies the major advantage of SC is the high-power density,high cycling life,and high peak current handling capacities.However,SC is also deficient in low energy density.The BESS is characterized by large energy density but low power capacity.To utilize the mutual advantages of two technologies,a hybrid ESS control model is modeled to overcome the limitations in using a single storage device for the optimal operation of MG technically and economically.To ensure secure and economic integration of PVs and WTs into the MG and for the reliability of the power system,accurate PV,WT power and load forecasting have become a critical element of energy management systems.Accurate forecasting can help improve the electric power quality of the electric power delivered to the electricity network and,and thus reduce the ancillary costs associated with general volatility.The main function of forecasting in an MG is to predict the demand of the loads in the MG network or the power generated by uncontrollable DERs(UCDERs)connected to the network for the near future.The scheduling of controllable DERs(CDERs)mainly focuses on the forecasted energies of UCDERs and load but UCDERS are highly uncertain and unpredictable.Even a small error in UCDERS forecasted outputs may result in great uncertainties for the real-time operation of MG given its limited scale and size.As isolated MG is our main area of interest therefore for the smooth operation of Isolated MG,we simultaneously focus on energy scheduling as well as reserve schedule.How over,forecasted and real-time output of UCDERs and load misinterpretation leads to unbalance the equilibrium of the isolated MG power system which leads to frequency excursion.Due to the capacity limitation of an individual MG,multiple neighboring MGs can be interconnected as MGs cluster to share energy and reserve for the sake of better performance of energy.This dissertation purpose energy management of isolated MGs-cluster,However,through scheduling cooperative energy sharing and real-time reserve sharing for ancillary resources,they have the potential to achieve better techno-economic efficiency by using joint energy and reserve sharing among MGs.A coalitional sharing scheme is formulated as an adjustable resilient optimization(ARO)problem in the day-ahead scheduling to optimally plan the energy and reserves of distributed generators(DGs)and energy storage systems(ESSs),thus reacting to the uncertainties of photovoltaic systems,wind turbines,and loads.These uncertainties are reduced by controlling the frequency in real-time and realizing the reserves in a distributed manner using a dynamic droop control mechanism.This control process is rooted in the ARO problem,which is conceived as an affine ARO problem and then transformed into a deterministic optimization problem that off-shore solvers can solve.Apart from minimizing operation costs,frequency restoration can be enhanced collaboratively,contributing to the MGs'combined techno-economic benefit to the alliance.In the face of climate change and rising energy costs,the push to improve energy efficiency is growing on a global scale,to seek out ways to reduce their operating costs and minimize greenhouse emissions.The Rise of cogeneration and tri-generation systems,also known as combined heat and power(CHP)or combined cooling,heat and power(CCHP)systems respectively,are being introduced to increase the efficiency of the power system.The Combined cooling,heating,and power(CCHP)systems based Micro-Grid(MG)provide a substitute to coups the energy concern issue such as energy scarcity,secure energy transmission and distribution.flue gas outpouring control,and economic stabilization and efficiency of the power system.The fluctuation of renewable energy sources(RS)and multiple load demands,i.e.,Electrical,Thermal,and cooling,challenges the CCHP based MG efficient economic operation.For diverse operating situations adaptability and to enhance the reliability and economic performance,deep reinforcement learning(DRL)is proposed for CCHP based MG in this dissertation.To reduce the operational cost(OC)and improve energy utilization the MG model is presented on a basis of the Markov Decision Process(MDP).For further enhancement and applicability to the energy management concern of MG,an improved DRL algorithm called Distributed proximal policy optimization(DPPO)is introduced.To find the optimal policies,the MG operator(agent)will be trained for a diverse operating situation for the efficient response to emergency conditions.Finally,a brief description of power system frequency stability of MG with and without energy storage system and the power system stabling time with BESS and with HESS is presented with case studies to investigate that our proposed system is efficient.The power system stability of isolated MGs cluster is presented with case studies to prove the effeteness of the proposed energy management system.In the end,this dissertation focuses on the economical aspect of isolated MGs cluster as well as the technology to improve the power system stability and also to operate at a point where its maximum efficiency can be achieved.