Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response. (15th November 2017) - Abdulaal, Ahmed; Moghaddass, Ramin; Asfour, Shihab
Graphical abstract: Highlights: Two-stage model links discrete-optimization to real-time system dynamics operation. The solutions obtained are non-dominated Pareto optimal solutions. Computationally efficient GA solver through customized chromosome coding. Modest to considerable savings are achieved depending on the consumer's preference. Abstract: In the wake of today's highly dynamic and competitive energy markets, optimal dispatching of energy sources requires effective demand responsiveness. Suppliers have adopted a dynamic pricing strategy in efforts to control the downstream demand. This method however requires consumer awareness, flexibility, and timely responsiveness. While residential activities are more flexible and schedulable, larger commercial consumers remain an obstacle due to the impacts on industrial performance. This paper combines methods from quadratic, stochastic, and evolutionary programming with multi-objective optimization and continuous simulation, to propose a two-stage discrete-continuous multi-objective load optimization (DiCoMoLoOp) autonomous approach for industrial consumer demand response (DR). Stage 1 defines discrete-event load shifting targets. Accordingly, controllable loads are continuously optimized in stage 2 while considering the consumer's utility. Utility functions, which measure the loads' time value to the consumer, are derived and weights are assigned through an analytical hierarchy process (AHP). The method is demonstrated for an industrial building model using real data. The proposed method integrates with building energy management system and solves in real-time with autonomous and instantaneous load shifting in the hour-ahead energy price (HAP) market. The simulation shows the occasional existence of multiple load management options on the Pareto frontier. Finally, the computed savings, based on the simulation analysis with real consumption, climate, and price data, ranged from modest to considerable amounts depending on the consumer's solution preference.