Optimal Coordination of Practically Constrained Demand Response Program and Distribution Network Reconfiguration

Abstract

Electric utilities and regulators are preparing for an electrified future with greater reliance on green energy. Building to peak demand is typical practice but becomes prohibitively expensive under deep electrification. Regulators are therefore contemplating methods to improve system utilization by reducing peak demand using flexibility tools such as demand response (DR) and distribution network reconfiguration (DNR). Practical DR programs for commercial and industrial users limit monthly activation frequency and duration, with higher costs beyond those limits. DNR causes additional wear on breakers and incurs costs. However, literature and utilities rarely address coordinated scheduling of DR and DNR, without coordination, DR may be used prematurely or sub optimally reducing long-term efficiency. The proposed framework co-optimizes DR activations and DNR switching decisions over a month-long horizon, using forecasted daily peak demand to minimize operational costs while respecting operational constraints, enabling a trend reversal in cumulative operational cost trajectories. Contribution 1 develops a DR model reflecting realistic utility constraints and consumer behavior. Contribution 2 formulates inter-temporal coordination of DR and DNR in a monthly mixed-integer nonlinear programming (MINLP) model. Studies on 12-Bus, IEEE 33-Bus and IEEE 69-Bus networks demonstrate that the co-optimization approach outperforms sequential daily optimizations, reducing operational costs by 47.56%, 29.77%, and 32.23% respectively, highlighting its effectiveness.