Multi-Dimensional Comfort and EV Stochasticity-Aware Optimization Strategy for Residential PV Storage Systems

Abstract

Residential photovoltaic–battery energy storage systems (PV-BESSs) and electric vehicles (EVs) expose households to a persistent conflict between electricity cost minimization and multi-dimensional comfort. This paper proposes a day-ahead joint scheduling strategy for a PV-BESS, flexible household loads, and a vehicle-to-grid (V2G) capable EV that explicitly quantifies this trade-off. Thermal comfort and temporal preference satisfaction are formulated as bounded indicators and aggregated into a single electricity-consumption comfort index through the efficacy coefficient method. EV availability is characterized by a probability-distribution-based travel model, in which Monte Carlo sampling of the daily mileage and return time is decoupled offline from the optimization so that the conditional charging and discharging logic reduces to linear constraints, and a two-stage scenario-based extension with a chance constraint on the departure energy requirement co-optimizes the household schedule against travel uncertainty. The economic and comfort objectives, explicitly incorporating a cycle-based battery degradation cost to accurately evaluate V2G arbitrage profitability, are combined by a normalized weighted-sum scalarization, and the resulting mixed-integer program with convex quadratic comfort constraints is solved to proven global optimality by GUROBI through YALMIP. For a typical summer household in Southern China, the proposed strategy reduces the daily electricity cost by approximately 7.3% (from 10.69 CNY to 9.91 CNY) at the price of a moderate decrease in the comfort index from 0.86 to 0.77. A sensitivity study of the peak–valley penalty coefficient further shows that flattening the load profile by 1.5 kW raises the user’s daily cost by 2.4 CNY, quantifying the compensation a distribution utility would need to offer to elicit load smoothing. To account for the inevitable forecasting errors of PV generation and outdoor temperature without incurring prohibitive computational burdens, conservative reserve margins and adaptive thermal bounds are introduced into the formulation. Relative to the deterministic representative-scenario schedule, the stochastic extension reduces the expected out-of-sample cost while raising the probability of meeting the EV departure energy requirement from 91.4% to 98.3%. Furthermore, comprehensive benchmark comparisons against pure cost minimization, pure comfort maximization, and deterministic schedules reveal the superiority of the proposed multi-dimensional index over conventional one-dimensional penalties in balancing household economics and occupant satisfaction.