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Advances in metering and two‐way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

With development of two‐way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air‐conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K‐medoids‐based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level. [ABSTRACT FROM AUTHOR]

Deployment of battery energy storage (BES) in active distribution networks (ADNs) can provide many benefits in terms of energy management and voltage regulation. In this study, a stochastic optimal BES planning method considering conservation voltage reduction (CVR) is proposed for ADN with high‐level renewable energy resources. The proposed method aims to determine the optimal BES sizing and location to minimise the total investment and operation cost considering energy saving achieved by CVR, while satisfying system operational constraints in the presence of stochastic renewable power generation. The uncertainty of load composition is also modelled through scenario analysis. The proposed planning scheme is tested in a modified IEEE 15‐bus system and 43‐bus radial system, respectively. The numerical results validate that the combination of CVR and BES can achieve more energy savings. [ABSTRACT FROM AUTHOR]

This study presents a two‐stage planning framework of the battery energy storage system (BESS) and micro‐turbine (MT) in a microgrid. In the first stage, the optimal allocation decisions are made before the actual realisation of the operational uncertainties. In the second stage, the optimal operation strategies are made for the microgrid by minimising the costs paid to the main grid, fuel cells, MTs, BESSs and controllable loads (CLs). The hot water system and interruptible load are considered as CLs. Their mathematical models are built to investigate their roles in smoothing renewable energy. In addition, efforts are made to keep the linearity of the formulated optimisation problem, and the backward scenario reduction method is adopted to further enhance the computational efficiency. The modified IEEE 33‐bus radial system is used as a microgrid to verify the effectiveness of the proposed approach for both islanded and grid‐connected microgrids. Sensitivity analysis has been conducted to compare the economics and robustness of the identified solutions. [ABSTRACT FROM AUTHOR]