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This paper presents a novel coordinated Conservation Voltage Reduction (CVR) and Electric Vehicle Demand Control (EVDC) method for energy-efficient power system operation. The goal of coordinated CVR-EVDC is to minimise the total energy consumed by the distribution network through coordination of the scheduling of EV charging, the OLTC transformer tap position, and the switching of the capacitors on the network. Using publicly available statistics of journey length and travel time of car drivers in the UK, a stochastic model is developed for EV battery State-of-Charge (SOC) and its availability for home charging to generate EV charging demand profiles for different levels of EV penetration. These are then used in conjunction with Particle Swarm Optimisation (PSO) and a relaxation equality constraint to explore the potential for optimally coordinating CVR operation and the scheduling of charging of EVs on LV distribution networks. Results for five different LV network scenarios show that a coordinated CVR-EVDC approach enables more significant energy savings than with independent operation of CVR and EVDC – 4% greater with dumb charging and 3% greater with valley-filling smart charging. This study shows that distribution network operators can manage the networks by coordinating CVR and EVDC to manage consumer charging requirements for better energy savings.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

© 2024Vehicle-to-grid (V2G) charging, where vehicles can send power to the grid, can provide valuable services to energy systems and network operators. However, social acceptance is an essential and overlooked barrier which must be addressed if V2G is to be successfully deployed. This study investigates the factors that govern attitudes towards V2G, and how electric vehicle (EV) ownership and participation in V2G changes them. For the first time, this includes survey data from users who had experience using a V2G charger, comparing the response of V2G users (n=49) with EV owners (n=520) and non-EV owners (n=1091). We show that time and EV ownership have lowered concerns around range anxiety, and that EV ownership and V2G trial participation leads to a 15%–35% increase in stated willingness to participate in V2G or Smart Charging as compared to a 2013 baseline. Additionally, it is demonstrated that the strongest single predictor for V2G willingness is whether the consumer believes that V2G can contributes to a stable electricity system. These results suggest that education around V2G benefits and allowing consumers to test V2G before committing could be key factors in increasing adoption. We also highlight the importance of data privacy, which for some consumers contributes towards a negative attitude towards V2G. We release the raw survey data and code with this manuscript.