Search

To harmonise the operation of renewable and conventional power generation, the radial distribution network of islanded microgrids (MGs) has been regionalised into two types of regions namely conventional generation dominant regions (CGRs) and renewable generation dominant regions (RGRs). RGRs will operate on constant frequency control methodology and CGRs will operate using conventional control techniques. A framework starting from the planning stage where sensitivity analysis‐based approach has been implemented for regionalisation and then optimisation of distributed generation (DG) units has been carried out for regionalised MGs (RMGs) is proposed in this study. At operation stage, a short‐term dispatch framework has been proposed based on the forecasted wind power generation scenarios with a battery energy storage system in the RMGs. A power exchange strategy with a scheduled pattern between the regions has also been developed to make the scheme more dispatchable and reliable. Multi‐objective optimisation algorithms have been developed for DGs planning and power dispatch in RMGs. The performance of the proposed RMGs has been studied by implementing it with four different modes of operation by converting IEEE 15‐bus radial distribution system into an islanded MG. [ABSTRACT FROM AUTHOR]

Microgrids have been recognised as one of the most promising but challenging research topics over the last decade. The optimal energy scheduling problem is regarded as the most essential aspect in the tertiary level control in microgrids. However, most existing centralised or distributed scheduling models only focus on the logical and dynamical feature of microgrids' operation or the non‐linear power flow constraint, which would overact the system performance without considering appropriate unit commitment requirements. Moreover, applying decomposition and iteration technics to complex scheduling problems would encounter convergence issues. To address this concern, this study presents a mixed integer linear reformulation to characterise the operation of different controllable devices and convex relaxation techniques to cope with non‐linear power flow constraints, leading to a mixed integer second‐order cone programming framework in a concordant yet computationally efficient pattern, capturing non‐linear, logical and dynamical properties of the optimal energy scheduling problem in microgrids. The effectiveness of the proposed framework is validated on the IEEE 33‐bus distribution network with both grid‐connected and islanded modes. [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]

The smart distribution system architecture provides value‐based control techniques that facilitate bi‐directional power flows and energy transactions. Although consensus and understanding continue to develop around peer‐to‐peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two‐stage of power scheduling. The proposed model is verified on the modified IEEE 15‐bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk‐seeking operation strategy is recommended if wind power penetration increases. [ABSTRACT FROM AUTHOR]

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]

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]

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]

System overloading is becoming a critical issue in distribution system due to outdated infrastructure and growing electricity demand. Although renewable‐based distributed generation is a promising solution to relieve this issue, its intermittency and uncertainty impose significant challenges on system operations. This study attempts to coordinate networked energy storage systems (NESSs) to manage network loading in distribution networks. The NESS can act as a buffer to absorb surplus energy during high generation periods and serve the demand during peak load periods. A consensus‐based dispatch strategy is proposed to coordinate NESSs. Through limited communication among neighbouring NESSs, required active power curtailment is shared. The mathematical formulation of a state‐of‐charge weighting factor is introduced to improve the efficiency of NESSs. A sensitivity study is also conducted to demonstrate the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]