In this paper, an intelligent energy management framework with demand response capability was proposed for industrial facilities. The framework consists of multiple components, including industrial processes modeled by the state task network (STN) method, thermostatically controlled loads (TCLs) like the heating, ventilation and air conditioning (HVAC) system with chilled water storage (CWS), renewable generation like photovoltaic (PV) arrays and electric vehicles (EVs). These components were firstly modeled and the operation of them is then optimized in time-of-use (TOU) pricing schemes. Factors that affect several components at the same time, e.g. the number of workers, are considered. The optimization is formulated as a mixed integer linear programming (MILP) problem. A general tire manufacturing facility was investigated as the case study. Simulation results show that the proposed intelligent industrial energy management (IIEM) with DR is able to effectively utilize the flexibility contained in all parts of the facility and reduce the electricity costs as well as the peak demand of the facility, while satisfying all the operating constraints. Index Terms-Industrial demand response, industrial energy management, state task network, thermostatically controlled loads, chilled water storage, electric vehicles.

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the power consumption for commercial users. A multi-objective optimization model of energy management for commercial users is established based on the hybrid simulation of electricity market bidding. By running the multi-objective optimization model based on hybrid simulation, the results show that the proposed method can realize the optimization of energy management for commercial users considering electricity market bidding.

Abstract:In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed.

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