Combined Heat and Power Economic Dispatch for Isolated Microgrids (2019)
Microgrids (MGs) have gathered significant attention over the last decade due to their potential to integrate Renewable Energies (RE) into power systems, in a reliable and efficient way, and their ability to provide sustainable energy supply solutions for remote areas without a connection to the main grid. Microgrids used for the latter application are known as isolated MGs since they are permanently operating in stand-alone mode. Isolated MGs have specific technical features such as low inertia and a critical demand-supply balance constraint, which hinder their operation, especially in cases with high penetration of intermittent and fluctuating RE. Moreover, it is expected that Combined Heat and Power Systems (CHP) will play an important role in MGs since these systems can considerably improve the overall system efficiency. CHP systems introduce additional technical challenges mainly related to the heat-power dependency of CHP and the thermal and power demand uncertainty. Thus, in order to guarantee a reliable and economic operation of isolated MGs integrating CHP units, it is important to design adequate strategies and methods for their different control levels. In these microgrids, the Energy Management System (EMS) has the main function of optimizing their operation through the solution of optimization problems such as Unit Commitment (UC), economic dispatch and/or optimal power flow. Hence, this thesis seeks to address the aforementioned challenges by proposing a novel energy management system (EMS) approaches for isolated MGs with CHP units and high penetration of RE. First, an EMS algorithm is proposed, based on an Affine Arithmetic-based Unit Commitment (AAUC) problem for day-ahead dispatch, using uncertainty intervals of both load and RE to provide robust commitment and dispatch solutions in AA form, which are feasible for all the possible realizations within the predetermined uncertainty bounds. A real-time dispatch solution is then found by the proposed algorithm, which computes the noise symbols values of the affine forms obtained by the AAUC, based on the current and actual load, the RE power levels and the available reserves. If the actual forecast error is outside the uncertainty bounds considered in the AAUC solution process, leading to possible load and/or RE curtailment, the AAUC is recalculated with updated forecast information. The proposed AA-based EMS is tested on a modified CIGRE microgrid benchmark and is compared against day-ahead deterministic, Model Predictive Control (MPC), stochastic optimization, and stochastic-MPC approaches. The simulation results show that the proposed EMS provides robust and adequate cost-effective solutions, without the need of frequent re-calculations as with MPC-based approaches, or assumptions regarding statistical characteristics of the uncertainties as in the case of stochastic optimization. Finally, a novel approach for the optimal economic dispatch of CHP MGs is proposed, which incorporates an Affine Arithmetic-based Economic Dispatch (AAED) problem into an MPC framework. The proposed algorithm solves each ∆t minutes (e.g. 15m) an AAED problem with time steps of ∆t minutes over a time horizon T (e.g. 24h). It uses the available forecast and the current state of the system, to provide the schedule and the affine forms that represent the operation intervals of the generators and Energy Storage Systems (ESS) for the next time interval [t, t + ∆t]. Online set points for generators and ESS are then obtained by computing the noise symbols values of the affine forms, based on the most updated information of electricity and heat demands and available renewable energy power. A theoretical CHP-based MG, comprising PVs, a gas boiler, a CHP unit, a battery, and a thermal tank, is used to assess the performance of the AA-MPC approach in both connected and isolated operation modes. The method is also compared with a deterministic MPC approach. Results show the ability of the method to better address forecasting errors, resulting in more cost-effective solutions, without considerably affecting the computation performance.