The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.

Integrated energy system (IES) has received increasing attention in micro grid due to the high energy efficiency and low emission of carbon dioxide. Based on the technology of combined heat and power (CHP), this paper develops a novel operation mechanism with community micro turbine and shared energy storage system (ESS) for energy management of prosumers. In the proposed framework, micro-grid operator (MGO) equipped with micro turbine and ESS provides energy selling business and ESS leasing business for prosumers. Prosumers can make energy trading with public grid and MGO, and ESS will be shared among prosumers when they pay for the rent to MGO. Based on such framework, we adopt a cooperative game for prosumers to determine optimal energy trading strategies from MGO and public grid for the next day. Concretely, a cooperative game model is formulated to search the optimal strategies aiming at minimizing the daily cost of coalition, and then a bilateral Shapley value (BSV) is proposed to solve the allocation problem of coalition’s cost among prosumers. To verify the effectiveness of proposed energy management framework, a practical example is conducted with a community energy network containing MGO and 10 residential buildings. Simulation results show that the proposed scheme is able to provide financial benefits to all prosumers, while providing peak load leveling for the grid.

Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models.

Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.