• Energy Research

With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents’ energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical data.

In recent years, solar power technology and energy storage technology have advanced, leading to the increased use of solar power devices and energy storage systems in residential areas. Carbon management has become an important method to help the community manager guide energy consumption in a timely manner, effectively reduce the carbon emissions of the community, and reduce the substantial harm to the environment. This paper aims to study the issue of carbon management and resource allocation in an intelligent community with combined heat and power (CHP) systems and solar power. The presence of heterogeneous load demands in the power grid was considered. The main objective was to minimize the average system cost over time, which included the costs associated with the power grid and gas. The Lyapunov optimization theory was employed to solve the non-convex optimization problem of carbon management and resource allocation without energy sharing. To solve the energy-sharing problem, we designed an energy-sharing algorithm based on the Q-learning algorithm. Lastly, we conducted extensive simulations using actual trace data to validate the effectiveness of our proposed algorithms.

The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

Electric vehicles (EVs) have become popular in daily life, which influences carbon dioxide emissions and reshapes the curves of community loads. It is crucial to study efficient carbon emission scheduling algorithms to lessen the influence of EVs’ charging demand on carbon dioxide emissions and reduce the carbon emission cost for EVs coming to the community. We study an electric vehicle (EV) carbon emission scheduling problem to shave the peak community load and reduce the carbon emission cost when we do not know future EV data. First, we investigate an offline carbon emission scheduling problem to minimize the carbon emission cost of the community by predicting future data with regard to incoming EVs. Then, we study the online carbon emission problem and propose an online carbon emission algorithm based on a heuristic rolling algorithm. Furthermore, we propose a reinforcement learning smart carbon emission algorithm (RLSCA) to achieve the dispatching plan of the charging carbon emission of EVs. Last but not least, simulation results show that our proposed algorithm can reduce the carbon emission cost by 21.26%, 16.60%, and 8.72% compared with three benchmark algorithms.

Autonomous electric vehicles (AEVs) will become an inevitable trend in the future transportation network and have an important impact on the power grid. It is difficult to find the optimal distributed charging solution for AEVs to minimize the system cost with some uncertainties. In this paper, we investigate an AEVs charging and discharging problem with vehicle-to-grid (V2G) services. We aim to minimize the total electricity cost and battery degradation cost of AEVs and charging station batteries with V2G services, which takes the random arrival and departure of AEVs into account. We first propose a distributed charging framework of AEVs and charging stations by clustering method with the constraint of limited AEVs for each charging station in a region and formulate a distributed offline optimization problem. Then we formulate a distributed online charging optimization problem and propose a distributed online AEV charging scheduling (DOAS) algorithm to get an optimal charging solution. To study a more practical case, we reformulate the distributed online optimization problem with the uncertainties from base loads, renewable energy and charging demands. Furthermore, to improve the time efficiency of DOAS algorithm, we reduce the dimension of the distributed problem and design a dimension-reduction DOAS (DDOAS) algorithm. To seek a robust solution with some uncertainties, we propose a DDOAS algorithm with DRO based on Wasserstein distance (DDODW). Simulation results show that DOAS and DDOAS algorithms can have a close-to-optimal charging cost and a significantly less battery degradation cost of charging stations, compared with centralized online charging scheduling algorithm and DDOAS algorithm is more time-efficient than DOAS algorithm. The proposed DDODW algorithm can provide a robust solution for the energy schedule