Increasing deployment of distributed energy resources (DERs) is re-sculpturing the modern power systems in recent years. Future smart power distribution systems should be competent at accommodating extensive integration of DERs and managing the associated uncertainties at the distribution level. The electricity market has been proved to be an efficient way to employ market signals to direct behaviors of users and DERs with large capacity and homogeneous pattern. However, existing market frameworks cannot effectively handle a large number of small-scale DERs due to their diverse characteristics and arbitrary behavior patterns. In this context, an aggregated model which can represent and manage a diverse collection of DER, load, and storage is proposed. An additional trading platform, namely the energy sharing market, is established to reinforce the coordination and collaboration among various aggregators as well as operators. Energy sharing scheme is applied and a corresponding dynamic dispatch platform is designed to solve the crowdsource problem. The efficiency of the proposed model is validated by the numerical studies, and the market performance and impacts of energy sharing on the power systems are illustrated.INDEX TERMS Distributed power generation, electricity supply industry deregulation, energy management, energy sharing, crowdsourcing behavior.
Energy-related occupant behaviour in buildings has demonstrated considerable energysaving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models.This article proposes a holistic survey-and-simulation-based framework for estimating the energysaving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement program with effective and efficient measures.
Smart home scheduling, facilitated by advanced metering, monitoring, and manipulation technology, plays an important role in the energy transition in terms of accommodating intermittent renewable energy and improving energy consumption efficiency. The key functionalities of home energy scheduling are usually implemented by leveraging the flexibility of household appliances, such as thermostatically controlled loads (TCLs) and energy storage units, to improve the peak-to-average ratio for utilities and reduce energy bills for customers. However, the consumption patterns of appliances are greatly influenced by a variety of factors, including real-time tariffs, ambient temperature profiles, indoor activities, and solar irradiance. Hence, smart home energy scheduling is a challenging task because most of these impacting factors are stochastic and difficult to predict. To properly model and manage the uncertainty factors associated with smart home appliance scheduling, an economic model predictive control (MPC)-based bilevel smart scheduling scheme is proposed in this paper. The comprehensive modeling of distributed generation and household appliances is performed at the single-household level. The home energy scheduling problem is formulated on two levels, with the upper level emphasizing the economic impact and the lower level focusing on capturing TCL responses. The correlations among different TCLs and their performance under the influence of various uncertainty factors, such as environmental impacts and user behaviors, are considered. The efficiency of the proposed MPC-based bilevel optimization model and the effectiveness of the home energy scheduling strategy in managing uncertainties are validated and illustrated in numerical studies.
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