• Energy Research

IEEE Transactions on Smart Grid, 13 (4) ISSN:1949-3053 ISSN:1949-3061

The green energy transition calls for various solutions to enhance environmental sustainability. One of these is represented by renewable energy communities, which may help transition from centralized energy production to distributed renewable generation. European countries are actively promoting incentive schemes for energy communities to foster local electricity self-consumption in order to balance demand and renewable generation. In this context, energy storage facilities can be employed to gather the energy production surplus and use it in periods of low generation. In this paper, we focus on the optimal operation of an incentive-based energy community in the presence of energy storage systems. A centralized optimization problem was formulated to optimally operate storage systems at the community level. Starting from this solution, distributed charging/discharging commands were found to optimally operate the single storage units. Moreover, conditions guaranteeing the convenience of using energy storage systems inside the community were derived. Numerical simulations were performed to validate the reported results and to evaluate the potential benefits of energy storage facilities inside renewable energy communities.

Demand response is expected to play a fundamental role in renewable energy communities to alleviate the electricity demand–supply mismatch, especially in the presence of stochastic load and generation. In this paper, we consider an electric vehicle charging station that participates in incentive-based demand response programs. A real-time charging scheme is devised to optimize the charging station operation by coordinating the charging process of the electric vehicles, and complying with the incoming demand response requests. In this context, vehicle demand is assumed uncertain, while demand response requests ask for a change in the charging profile over certain time intervals, in exchange for a monetary reward. By exploiting the probability distributions describing the vehicle charging process, a stochastic formulation is employed to devise a novel charging algorithm aimed at reducing the charging station operational cost. Such a procedure can (i) handle the uncertainty affecting the charging process in different settings and scenarios, and (ii) exploit the information collected in real-time to refine forecasts and hence ensure a higher demand flexibility. Numerical results show that the proposed approach ensures considerable cost reduction compared to the benchmarks, and features highly scalable runtimes. Applied Energy, 373 ISSN:0306-2619 ISSN:1872-9118

In this paper, we study the problem of managing an energy community hosting a fleet of electric vehicles for rent. On the day ahead, service requests for electric vehicles are submitted to the community. Then, the optimal request-to-vehicle assignment has to be found, as well as the optimal charging schedule of vehicle batteries. A suitable model is presented and included in an existing energy community architecture. The overall community management problem is formulated as a bi-level model, featuring two nested optimization problems. The optimal request-to-vehicle assignment requires the solution of a mixed-integer linear program. To reduce the computational complexity, a heuristic solution to the assignment problem is presented. Numerical results show that participation in the community grants a remarkable reduction in the electric vehicle charging cost. The adoption of the heuristic assignment solution provides a dramatic reduction in the computation time required to solve the bi-level model. At the same time, the level of suboptimality introduced appears to be negligible, being less than 1% in most of the considered cases.

This paper deals with the problem of minimizing the electricity bill of smart buildings equipped with centralized heating systems and thermal and electrical storage devices. Building participation in a Demand-Response program in the form of price-volume signals is also considered. The proposed solution is based on a Model Predictive Control approach to operate the heating system and the storage devices in an optimal fashion, under thermal comfort and technological constraints.

The increasing adoption of electric vehicles (EVs) has left power network providers to deal with new challenges in terms of grid stability and electricity market design. On the latter direction, a demanding problem is represented by the development of probabilistic algorithms capable of computing optimal time-varying price profiles for EVs charging stations to induce a desired aggregative behavior. Here, the inclusion of demand elasticity represents a key feature to provide usable schemes for real-world cases. In this paper, we propose an "estimate-then-optimize" framework for optimal dynamic pricing computation in the presence of price-sensitive customers. It consists of an estimation step based on nonparametric kernel methods to infer about the demand elasticity, followed by an optimization step to maximize the expected daily profit. We describe the charging process via a probabilistic framework and we show the benefits of the proposed formulation via extensive numerical experiments.

In the current context of growing electrification of the transport sector, offering rental and sharing programs for electric vehicles is considered one of the strategies to achieve decarbonization targets. Such programs should be supported by suitable optimization tools to manage the vehicle fleet, and make rental provision profitable for its operator. In this paper, we consider a rental system having a single station for electric vehicle pickup and delivery. For this system, we address the operational problem of simultaneously assigning rental requests to vehicles and determining the charging policies during inactivity intervals. The objective is to maximize the profit for the operator by minimizing the costs for electricity. The considered problem is complicated by uncertainty regarding the battery energy level when a vehicle returns to the station. This leads to a chance-constrained programming formulation, where the request-to-vehicle assignment and charging policies are determined by minimizing electricity costs while ensuring that the energy demand of the served requests is met with a prescribed high probability. Since the formulated mixed-integer problem with probabilistic constraints is hard to solve, a suboptimal approach is proposed, consisting of two sequential steps. In the first step, request-to-vehicle assignment is accomplished via a suitably designed heuristic procedure. Then, for a given assignment, the charging policy of each vehicle is determined by solving a relaxed chance-constrained problem. Numerical results are presented to assess the performance of both the assignment procedure and the optimization problem which determines the electric vehicle charging policies.

Abstract The increased penetration of plug-in electric vehicles asks for efficient algorithms to be adopted in parking lots equipped with charging units. In this paper, the peak power minimization problem for a plug-in charging station is addressed. A chance constraint approach is adopted in order to minimize the daily peak power, allowing for a tolerance on the charging service customer satisfaction expressing the probability that a vehicle leaves the station violating the agreed level of charge. Numerical simulations are provided to evaluate the performance of the proposed approach as well as to make a comparison with other techniques.