Fair Management of Vehicle-to-Grid and Demand Response Programs in Local Energy Communities

Abstract

Electric Vehicles (EV) are emerging in electricity grid, where the Vehicle-to-Grid (V2G) feature is a major flexibility opportunity for Demand Response (DR) programs. Optimized and fair management of EVs flexibility activation is then required. In the present paper it is proposed a methodology to deal with the complex management of the Local Energy Communities (LEC) with such resources. The method allows the fair selection of DR and V2G participants. Focusing on the EVs, it is compared two approaches: performance rate and clustering groups (considering extrinsic and intrinsic characteristics using modeling of preferences). The method was tested in five office buildings with a shared EV parking lot. Four different events were studied, and the results show that the performance rate might not be enough to have the best results from both the local manager and V2G participants. According to the numerical results, using the performance method the total reduction obtained was 60.20 kW confronting with the 105.13 kW reduced Power from Demand Response active consumer c on period t (kW).

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

“…Then, for the training process, we randomly extract a mini-batch sample of transitions from the replay memory D of the size of the batch (lines [16][17][18][19][20][21][22][23][24]. This step is only performed once there are enough transitions in the memory to cover the batch size.…”

“…In Formula (17), n is the number of energy values corresponding to the time interval during which the EVs' scheduling is performed; E target (t i ) and E EV (t i ) are the energy data points in the goal energy curve and the curve resulted by scheduling EVs at charging stations; and E target and E SV are energy sample means, computed as follows:…”

“…However, it still faces open challenges related to the complexity of the pricing structure that involves hourly or time-of-use rates, making EV owners reluctant to engage and requiring EV owners to adapt their behavior based on fluctuating electricity prices [16]. Other solutions are leveraging on the setup of demand response programs for EVs aiming to manage and modify their charging or discharging patterns based on grid conditions [17]. However, this presents a major challenge: the need to balance energy production and consumption in the face of intermittent and non-programmable energy sources which are strongly influenced by atmospheric conditions, making this difficult to predict [2].…”

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

“…V2G allows EV batteries to act as decentralized energy storage during high-demand periods, which is critical to reducing peak loads on the grid, aggregating solar and wind sources, reducing infrastructure investment, and promoting demand response [14]- [18]. The increasing number of EVs and EV users makes V2G a better solution than expensive peak load power plants [19].…”