Unified active and reactive power dynamic economic and emission dispatch of microgrids

Abstract

The exchange of power among the microgrid (MG), electric vehicles (EVs), energy storages (batteries), and the utility grid is a great challenge in the formulation of the optimal scheduling of the MGs. Furthermore, considering the unit commitment (UC) with the uncertainties that derive from the fluctuations of the renewable generation, open market pricing (OMPs), demand side, and the EVs, result in a significantly complex optimisation problem. Optimised operation of the MGs can result in enormous economic benefits to both the users and the environment. Therefore, there are considerable interests to develop algorithms and approaches to formulate and solve the optimisation problems of the MGs efficiently. In this research, a novel multi-period security-constrained unit commitment unified active and reactive dynamic economic and emission dispatch (SCUC-UARDEED) of the connected and isolated MG is presented. The formulation of the UC is developed and extended to accommodate both the active and reactive power of the distributed generators (DGs). The emission costs of the greenhouse gases in keeping with the emission level constraints are considered in the proposed optimisation problem to reduce the emission of the pollutant gases and achieve a low emission energy system. The overall formulation of the proposed SCUC-UARDEED of the MG takes into consideration the models of the reactive power production cost of the DGs, fuel cost, environmental costs, battery degradation cost, start-up and shutdown costs of the DGs, maintenance cost of the DGs, and the cost of the renewable power generation. The proposed optimisation of the MG is subjected to a comprehensive set of constraints, including active and reactive power security of supply for the connected and isolated MG and emission limit constraints. The impacts of the battery on the scheduling problem of the MG are determined by comparing scenarios with and without battery. Similarly, the impacts of the security of supply constraints are analysed. Uncertainties resulting from the fluctuations of the renewable generation and OMPs are modelled and incorporated into the scheduling problem of the MG as a two-stage stochastic optimisation with taking into account the aforementioned models of the cost functions and constraints. Integration of the active and reactive demand side management (DSM) with the SCUC-UARDEED of the connected and isolated MG is addressed. The DSM is considered as a separate appliance with an operation cycle, and it is considered as a decision variable within optimisation problems. Different types of the DSM techniques are applied to the various types of loads simultaneously under deterministic and stochastic environments. Accordingly, novel approaches and techniques are proposed in the thesis to allow the analysis and detailed investigation of the impacts of the DSM on the optimal scheduling of the MGs, the UC results, the exchanging active and reactive power with the utility grid, the system loads, the spinning reserve, and the secure supply of the MG. The uncertainties arising from both the generation and demand side are systematically modelled and incorporated into the optimisation problem in a two-stage stochastic approach with consideration given to the above cost functions and constraints, where the DSM is considered as a source of uncertainty. A novel scheduling strategy is proposed to integrate the EVs with the SCUC-UARDEED of the connected and isolated MG. The EVs charging and discharging operations are considered as decision variables in the optimisation approaches. The EVs are incorporated with the grid as bidirectional, grid to vehicle (G2V) as energy storage and vehicle to grid (V2G) as energy source. This integration of the EVs with the scheduling problem of the MG that includes all aforementioned cost functions and constraints increases the complexity of the optimisation problem. Accordingly, the economic models involved in the integration of the EVs with MG are developed, and a variety of charging and discharging scenarios are conducted to analyse the impacts of the EVs on the optimal scheduling of the MGs. The uncertainties resulting from the availability of the EVs and fluctuation of the generation of the renewable energy resources are modelled and incorporated with SCUC-UARDEED into a two-stage stochastic optimisation approach.