Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty

Abstract

Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.

Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs

“…Nevertheless, the unpredictability of PEVs charging and discharging is particularly challenging for the optimal planning of power grid operations. As a result, V2G implementation is a demanding task that requires the application of cutting-edge optimization techniques to consider all constraints that must be satisfied in such complex energy scheduling problem [18,19], while trading-off objectives of distinct nature and energy supply and demand sides. A comprehensive review on scheduling methodologies and mathematical optimization approaches for the V2G implementation is provided in Ref.…”

“…In Equation 18, P LDG j and P LDG j are the upper and lower bounds, respectively, for the electric power of the jth LDG unit. The ramp up and ramp down restrictions for the LDG units are given by constraint (19) [25,31].…”

How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles

“…For a single user, demand charge management via V1G can synchronize the charging to the over-generation of the roof-mounted photovoltaic plant so to maximize self-consumption [47]; similarly, V1G can apply a time-of-use tariff in order to reduce the electricity bill [48]. Similarly, demand charge management via V1G can coordinate the charging of electric vehicles in a car park [49,50] or in a narrow geographical border [51], applying machine learning methods [52,53], taking into account the users' preferences [54] or the batteries' state of health [55], thus limiting the demand during peak hours and, in general, providing valuable grid services to network operators. Given the different impact of V1G and V2G on the battery charging infrastructure and economics, today's investments are mainly aimed at supporting the massive deployment of electric vehicles and to ensure the extensive presence of charging points with one-way chargers.…”

Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme

“…In the first case, EV charging/discharging scheduling problem is usually considered as a sequential decision-making problem, which is modeled as the Markov decision process (MDP) [9][10][11][12][13][14] and then solved by dynamic programming [15]. The second case involves the use of conventional numerical optimization methods, such as linear programming or convex optimization [16][17][18][19][20][21][22][23][24]. However, these optimization-based methods have their drawbacks: they require mathematical model with some assumptions that are hardly known in practice.…”