Model predictive control of an ESS

MPC controllers are commonly used within microgrids and low voltage network applications that involve high uncertainties in demand applications, to decrease the operation costs and increase system efficiency. For example, Rowe et al. [33] presented an MPC controller that allows distribution network operators to control the energy storage systems under operational constraints on a low voltage network that feeds domestic customers. The MPC controller integrates a deterministic forecast with the objective of maximising the peak reduction of the distribution network [33]. The simulation results show that the MPC controller, on average, outperformed the set-point controller for a LV model of the domestic customers demand profiles (aggregation demand). An MPC technique with an application in microgrid systems has been discussed [45]. As in the study of Rowe et al., Oh et al. [45] developed an optimisation scheme for an islanded microgrid, using an MPC strategy. The use of an MPC in controlling diesel power sources and renewable sources with an ESS successfully manage to minimise the operational cost [45].

Stochastic load forecast with a scenario-based MPC model of a microgrid system with Electric Vehicle (EV) integration was presented in [46]. The aim of the optimal management controller presented by Ji et al. was to optimise the economic performance of the electricity demand and the EV charging demand. However, one forecast model [46], was developed based on having knowledge of the EVs charging schedules in advance, so they assumed that they would know when the electric vehicle needed charging. In cases where the charging requests are unknown, the MPC controller applied the worst-case scenario and set the boundary at the greatest possible charging load. Zhang et al. [28], also presented a Model Predictive Control (MPC) strategy for microgrid systems. The objective of the control model was to minimise the electricity energy costs in a microgrid system with renewable energy resources and an ESS. The optimisation time interval for the proposed model was one-hour resolution for a one-day time horizon and the battery efficiency was assumed to be one (no power losses). In order to explore the forecast error impact on the energy management system, the MPC controller was tested under different levels of forecast errors. The analysis of the results showed that increasing the forecast errors, increased the energy costs. The MPC helped to reduce the energy cost compared to a fixed day-ahead programming control model that does not consider any update for control and prediction data variables. The research in [28] did not focus on the forecast methods or use actual forecast data, and instead, they used a simulation model to

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generate different levels of forecast errors. As previously discussed, ESSs play a key role in LV network support such as power levelling and load shifting. To meet the demand scheduling of the grid, Xiong et al. [47] presented a real-time MPC to optimise the power flow for a wind farm system equipped with an ESS. The objective function of the MPC controller contained three sub-objectives with control and prediction horizon size equal to 24 hours: firstly, it aimed to reduce the impact of a wind curtailment factor; secondly, to increase the ratio of generated wind power fed to the power grid; thirdly, to maintain the generation of power to the grid. The MPC controller successfully followed the power plan of the electrical power grid system [47]. Instead of using real forecast data, the research in [47] added a random noise to actual demand data. In other work, MPC controllers have been used for large scale ESSs located at wind power plants to improve the energy dispatchability [48]. The simulation results have shown that an ESS with model predictive control (MPC) can reduce the generation plan errors to meet the power grid needs by approximately 80% [48]. The proposed MPC model tested by using 7 days of data and the forecast wind farm generation demand was provided by a third-party, the Bonneville Power Administration. In general, these sets of studies show the potential effeteness and capabilities of using MPC as an ESS control technique.

Uncertainty in MPC system

The volatile and stochastic demand behaviour on electrical distribution network applications increase the challenge of accurately predicting the LV demand. As discussed in the previous section, forecast error and uncertainty have a significant impact on MPC energy storage control algorithms. Uncertainty and forecast error impacts on MPC solutions have been discussed in the literature [28] [46] [49]. The research, in [50], formulated a hybrid renewable energy system with battery energy storage in a family residential home, using an optimal energy operation strategy based on an MPC algorithm to minimise the energy costs and meet the electricity demand. Due to the high level of uncertainty regarding weather conditions that effect the renewable sources output, Wang et al. [50] used real time hourly weather forecast data to reduce the impact of uncertainty. For customer energy demand side, a day-ahead demand scheduling algorithm was used to generate the energy consumption for a single-family house. This schedule aimed to shift the flexible loads, such as washing machines, to match renewable generation output. Both the real time weather forecast, demand response schedule, and rolling updates helped the MPC control model to handle the uncertainties and increase the overall

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system performance. This is showed the significant of using rolling forecast model to minimise the error impact of the optimal control performance. Wang et al. [50] assumed that the electricity consumption of a residential home was perfectly forecast in order to match the renewable output. In the research presented in this thesis, an imperfect forecast model for highly volatile demand is considered, unlike the study of Wang et al. [50]. Forecast errors in the prediction demand model used in an MPC controller are discussed in [51]. Holjevac et al. presented a microgrid system including electricity demand and energy storage that operated to meet consumer needs and minimise costs by using a receding horizon controller. The Holjevac et al. work [51], showed that the efficiency of the energy operation model depends on demand and generation prediction output, and daily correction of the MPC controller schedule. The operating horizon size for the MPC model was one day ahead with 48-time steps similar to [49]. The corrective schedule aimed to update the initial operation points, this helped to reduce the impact of the forecast errors by updating the demand and control model data at successive time steps. However, the receding horizon controller was designed to minimise the energy costs only based on the energy and balancing prices and did not investigate the peak demand reduction for the households in the network.

The previous set of literature, discussed in this section, presents the impact of uncertainty and forecast error in RH controller and shows the significance of studying this impact. Furthermore, it showed a considerable evidence supporting the capability of forecasts to improve optimal energy management controller performance [49] [50]. There are a limited number of studies that have discussed the impact of uncertainty in LV applications or electrical industrial demands with regards to ESS performance.