Joint Demand Response and Energy Trading for Electric Vehicles in Off-Grid System

Abstract

This paper proposes a joint demand response and energy trading for electric vehicles in an off-grid system. We consider isolated microgrid in a region where, at a given time, some renewable energy generators have superflous energy for sale or to keep in storage facilities, whereas some electric vehicles wish to buy additional energy to meet their deficiency. In our system model, broker lead the market by determining the optimal transaction price by considering a trade-off between commission revenue and power reliability. Buyers and sellers follow the broker's decision by independently submitting a transaction price to the broker. Correspondingly transaction energy is allocated to the buyers in the proportion to their payment, whereas the revenue is allocated to the sellers in proportion to their sales. We investigated the economic benefits of such a joint demand response and energy trading by analyzing its hierarchical decision-making scheme as a single-leader-heterogeneous multi-follower Stackelberg game. With demonstrating an existence of a unique Stackelberg equilibrium, we show that the transaction price in the proposed market model is up to 25.8% cheaper than the existing power market. In addition, we compare the power reliability results with other algorithm to show the suitability of proposed algorithm in the isolated microgrid environment.

Sustainable Electric Vehicle charging scheme: User requirements in designing energy trading algorithm

“…In particular, different literature have identified the objectives/requirements from different perspectives. For instance, some researchers have focused on the requirements of individual participants [25,26], whereas some have focused on the overall system (entire market) requirements [27,28] or both individual and overall system requirements [14]. However, irrespective of the perspective and the participant category (EV user or household with solar PV systems), many authors have attempted addressing the economic requirements in trading mentioning that 'most profitable and long-term sustainable local energy market business models should focus on financial benefits as their main value proposition'.…”

“…In relation to EVs, [5], [19] and [29] have considered cost minimization whereas [14], [25] and [30] have considered utility maximization in algorithm designing. However, authors have discussed other service-related requirements such as reliability [26,31], availability [8], distance minimization [10] and privacy preservation [32] less frequently. Similarly, in household prosumer research, authors have widely considered financial requirements of revenue improvement [33], and utility improvement [26,34] and barely considered user experience related requirements such as reliability and privacy and security of data [31].…”

A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions

“…Stochastic Programming [94,95] MILP [87][88][89][90] Convex Optimization [91,92] Meta-heuristic [93] Game Theory [48][49][50][51][52][53][54], [57,58] Q-Learning [80] Lyapunov Optimization [96] FIGURE 4: Energy trading in the smart grid: A Problem Taxonomy a bottom-layer optimization describes the energy trading decisions by customers and producers according to the price announced. In contrast, a top-layer optimization describes the gain of local energy traders with the benefits of energy consumers and providers.…”

“…However, EVs are sensitive to the decisions taken by their owners, which specifies their charging/discharging rates and the payments. For example, the authors in [57] proposed a both models, such as DR management and energy trading for EVs in an off-grid system. The hierarchical decision-making scheme of this model has been analyzed as a single-leaderheterogeneous multi-follower Stackelberg game.…”

Joint Optimization of Quota Policy Design and Electric Market Behavior Based on Renewable Portfolio Standard in China

“…The most important difficulty is how to get the game solution under different policy parameters which best accord with reality. The existing strategies for solving this problem can be divided into: 1) completely rational game [13], [14], that is, it is assumed that the players of the game participate in the decision that has the greatest benefit to their side from the beginning to the end, such as Cournot model [15], [16], Stark model [17], [18], etc. ; 2) The bounded rational game [19], [20], that is, the player of the game needs to continuously learn the strategy rather than realize the optimal choice at one time.…”