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The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

The evolution of smart microgrid and its demand–response characteristics not only will change the paradigms of the century-old electric grid, but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed renewable energy generators. In this paper, we propose an optimization scheme to minimize the electricity price with a framework for optimal trading of energy between sellers and buyers of the microgrid network (MGN). The proposed scheme is capable of solving the optimal power allocation problem for an MGN in a polynomial time without modifying the actual marginal costs of a generator. Initially, we mathematically formulate the problem as nonlinear nonconvex and later decompose the problem to separate the optimal marginal-cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal-cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with various cost functions and compare it with a first come first serve electricity allocation method and distributed energy trading for multiple microgrids.

To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources (RES) and plug-in electrical vehicles (PEVs). By using PEVs for electricity storage and RES as distributed generators, microgrids (MG) become more reliable, stable, and cost-effective. However, the high intermittent nature of energy sources and unpredictable presence of PEV in the MG offers new technological challenges to the smart grid/MG energy management system (EMS). In this paper, we propose a new distributed real-time electricity allocation (DRTA) scheme for the smart grid/MG EMS whose objectives are to reduce the electricity bill of the residential customers, to increase the overall social benefit of smart MG/grid community, and to increase the energy efficiency and reliability of the MG to rely on locally generated electricity. We formulate the problem as a noncooperative game using mechanism design and solve it to optimality. The proposed DRTA scheme converges to an optimal Nash equilibrium and produces a near-optimal solution. We compare the proposed scheme with a centralized optimal electricity consumption scheme and to the solution obtained from unregulated (natural) scheduling framework.

The integration of renewable energy sources and electrical vehicles (EVs) into microgrids is becoming a popular green approach. To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources and EVs. By using EVs as electricity storage and renewable energy sources as distributed generators (DGs), microgrids become more reliable, stable, and cost-effective. In this paper, we propose an optimal centralized scheduling method to jointly control the electricity consumption of home appliances and plug-in EVs as well as to discharge the latter ones when they have excess energy, thereby increasing the reliability and stability of microgrids and giving lower electricity prices to customers. We mathematically formulate the scheduling method as a mixed integer linear programming (MILP) problem and solve it to optimality. We compare the optimal solution to that obtained from a scheduling framework, where EVs do not have discharge capabilities, decentralized charge control using game theory and to a solution obtained from a naive scheduling framework.

The evolution of microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new scenario, once always energy consumers, now may act as sellers due to the excess energy generation from the newly deployed distributed generators (DGs). In this paper, we propose a novel mathematical model and its solution methodology to minimize the overall electricity price. The model is nonlinear and nonconvex due to the nonlinear and nonconvex characteristics of the marginal costs. To solve the minimum electricity price model (MEPM) problem efficiently and optimally, we decompose the problem into two subproblems: overall marginal cost problem (OMCP), and minimum cost allocation problems. A solution of OMCP determines the boundary of the overall marginal costs, and the allocation problem maps the optimal amount of energy from sellers to buyers for minimum price. The MEPM solution uses a divide-and-conquer method that divides the overall marginal cost boundary (solution OMCP) into two and determines the allocations for minimum cost, interactively. We compare MEPM with a first come first serve pricing scheme.