This study presents a multi-objective problem of the optimal power management of micro-grids, which is based on modified Tribe-PSO. Minimising total cost and emission over a 24 h time horizon are the objective functions in this problem. For the participation of consumers in the management of micro-grids, demand response program is used. The scheduling approach is tested on the IEEE standard distribution network with 33 buses and is compared in two modes: grid-connected and stand-alone. Simulation results show that the presented power and demand scheduling provide a lower operation cost and more efficient utilisation of the renewable energy sources and the demand response programs.

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context.

This study presents a demand response (DR) model to curtail the load during peak hours in a secondary (230/440 V) distribution system of Tamil Nadu Generation and Distribution Corporation (TANGEDCO), a state-owned enterprise, India. TANGEDCO penalises utilities if they violate their permitted contractual limit of demand. Conventional demand control usually curtails a specific region of the secondary distribution network. The limitation of the complete blackout of the particular region in the distribution network increases the loss of load probability. Hence, this project implements an economic DR model in real time using demand side forecasting and by scheduling air-conditioning loads based on their priority. The pilot project is executed and is monitored at the National Institute of Technology, Tiruchirappalli, India campus. A standard back propagation neural network is used for forecasting a 15 min interval ahead maximum demand in kVA. This economic model comprises of a communication network that uses ON/OFF switching wirelessly controlled relay modules. Finally, the benefits and the strategy involved in the project are presented. It is found that the proposed scheme prevents the electrical demand from exceeding the contractual limit, whereby the penalty due to the violation is zeroed when compared to the previous year.

In order to fully consume renewable energies and schedule demand-side resources more hierarchically, a double-deck optimal schedule model of micro-grid, which connects to power grids and concludes battery energy storage system (BESS), is proposed. Unlike the original peak-valley time-of-use (TOU) price, in the upper layer optimal schedule, the improved TOU price which takes account into the user's satisfaction can express the adjusted loads accurately and the resulting net loads can be treated as a link between upper and lower layer scheduling. For the reason of having no precise model of BESS, the lower model for the goal of minimising the operation cost is solved by action dependent heuristic dynamic programming algorithm that is not relying on the accurate controlled object model. This algorithm is used to obtain the most optimal performance index function and control strategy by the optimal iterative process, which is based on the back propagation neural network used for evaluating the optimal performance index. Analysis of examples and results has been presented to show the effectiveness of the proposed strategies.