Abstract
This paper proposes an efficient energy management system (EMS) for industrial microgrids (MGs). Many industries deploy large pumps for their processes. Oftentimes, such pumps are operated during hours of peak electricity prices. A lot of industries use a mix of captive generation and imported utility electricity to meet their energy requirements. The MG considered in this paper includes diesel generators, battery energy storage systems, renewable energy sources, flexible loads, and interruptible loads. Pump loads found in shipyard dry docks are modelled as exemplar flexible industrial loads. The proposed EMS has a two-stage architecture. An optimal MG scheduling problem including pump scheduling and curtailment of interruptible loads (ILs) is formulated and solved in the first stage. An optimal power flow problem is solved in the second stage to verify the feasibility of the MG schedule with the network constraints. An iterative procedure is used to coordinate the two EMS stages. Multiple case studies are used to demonstrate the utility of the proposed EMS. The case studies highlight the efficacy of load management strategies such as pump scheduling and curtailment of ILs in reducing the total electricity cost of the MG.
Highlights
Industrial power networks may comprise distributed generators, battery energy storage systems (BESSs), and different types of loads
The energy management (EM) problem simplifies to a unit commitment (UC) problem which is usually formulated as a mixed integer linear programming (MILP) or mixed integer quadratic programming (MIQP) problem
Auxiliary Pump 1 operates during hours 15, 17, and 18; Auxiliary Pump 2 operates during hours 11 and 15; Auxiliary Pump 3 operates during hour 15; and Auxiliary Pump 4 operates during hour 11
Summary
Industrial power networks may comprise distributed generators, battery energy storage systems (BESSs), and different types of loads. The EMS is capable of incorporating load management strategies in its optimal scheduling problem formulation. The EMS needs to consider power flow equations and system security constraints while optimally dispatching the MG components. This is considered in an optimal power flow (OPF) problem incorporated in the EMS architecture. Formulating an efficient energy management (EM) problem is a complex task due to the need to integrate both unit commitment (UC) and OPF problems. This normally results in the EM problem being formulated by ignoring network losses and system security constraints. The feasibility of results obtained using such formulations is questionable owing to potential violations of the network constraints and the absence of power losses in the formulation
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