This paper proposed a Peer-to-Peer (P2P) local community energy pool and a User Dominated Demand Side Response (UDDSR) that can help energy sharing and reduce energy bills of smart community. The proposed UDDSR allows energy users within the community to submit flexible Demand Response (DR) bids to Community Energy Management Scheme (EMS) with flexible start time, stop time and response durations with regard to users' comfort zones for electric heating systems, electric vehicles and other home appliances, which gives maximum freedom to the DR participants. The scheduling of the DR bids, originally a multi-objective optimization problem (maximize the total flexible demand and the flexible demand in every interval during the whole DR duration), is transferred to a single objective optimization problem (maximize the total demand with penalty for demand imbalance during the whole DR duration) that can significantly decrease the computational complexity. Furthermore, to facilitate efficient energy usage among neighbourhoods, a local energy pool is also proposed to enable the energy trading among users aiming to facilitate the usage of surplus energy within the community. The electricity price of energy pool is determined by the real-time demand/supply ratio, and upper/lower limit for the price is configured to ensure the profitability for all the participants within the pool. To evaluate the performance of proposed UDDSR and local energy pool, comprehensive numerical analysis is conducted. It is found that the energy pool participants without PV can get at least 6.16% savings on electricity bill (when PV penetration level equals to 20%). The energy pool participants with PV can get much better return (at least 13.4% profit increase) on the PV generation compared to the conventional Feed-in-Tariff. If energy users join the UDDSR scheme, the participants can get further return, and the proposed UDDSR can provide a constant load reduction/increase during the every time interval of the whole DR event. If Battery Energy Storage System (BESS) is included in the DR operation, the usage efficiency of customers' flexible loads can achieve more than 85%.

Electric vehicles (EVs) are expected to play a critical role in future transportation systems. A number of countries have published roadmaps aiming to facilitate the adoption of EVs on the road. It is estimated that existing charging facilities will not be able to satisfy the tremendous charging demands of a dramatically increasing number of EVs. Following the rapid development of artificial intelligence and mobile communication technology, certain charging pricing mechanism is expected to influence the charging behavior of EV drivers. In order to maximize the working efficiency of highway charging facilities and the consumption of the renewable energy near charging facilities, this paper proposes a pricing methodology taking into account the charging facility service ratio, traffic flow and renewable energy generation. To support the adoption of the proposed pricing methodology, forecasts of hour-by-hour traffic flow and renewable generation as well as calculation of the shortest paths to different charging stations (CSs) are investigated. A road network testbed based on the Dublin traffic network is established to evaluate the proposed pricing methodology. It is discovered that for certain wind-rich CSs, the proposed pricing methodology can increase the consumption rate of wind energy by up to 82.97%, with an average improvement of 30.73%; for certain solar-rich CSs, it can improve the level of solar energy consumption by up to 59.50% and an average increase of 29.28% is achieved. The proposed pricing methodology can also reduce traffic jams to some extent at both peak and off-peak times. INDEX TERMS Electric vehicle, charging stations, charging pricing methodology, renewable energy consumption. NOMENCLATURE

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.