• Energy Research

Thanks to the advantages of zero carbon dioxide emissions and low operation cost, the number of on-road electric vehicles (EVs) is expected to keep increasing. They usually get charged through charging stations powered by either the grid or renewable plants. Due to the potential difference in electricity price between the grid and the renewable plants, an EV may purchase electricity at charging stations powered by renewable plants, and then discharge the surplus energy in the battery to the grid, to gain profits and enhance the overall renewable energy utilization. In this work, we aim to optimize the route selection and charging/discharging scheduling to improve the overall economic profits of EVs, taking into account the constraints, including the time-varying energy supply caused by the intermittent generation of renewable energy, the limited number of charging piles in a charging station, and the traveling delay tolerance of EVs. Firstly, a time-expanded vehicle-to-grid graph is designed to model the objective and related constraints. Then, we apply an AI-based A* algorithm to find K-shortest paths for each EV. Finally, a joint routing selection and charging/discharging algorithm, namely, K-Shortest-Paths-Joint-Routing-Scheduling (KSP-JRS), is proposed to minimize the total cost of EVs by maximizing their revenue from energy discharging under time constraints. The proposed approach is evaluated using the real traffic map around Santa Clara, California. The simulation, with different numbers of testing EVs, shows the feasibility and superiority of the proposed algorithm.

Smart sensing and wireless communication technologies enable the electric power grid system to deliver electricity more efficiently through the dynamic analysis of the electricity demand and supply. The current solution is to extend the traditional static electricity pricing strategy to a time-based one where peak-time prices are defined to influence electricity usage behavior of customers. However, the time-based pricing strategy is not truly dynamic and the electricity resource cannot be optimally utilized in real time. In this paper, we propose a usage-based dynamic pricing (UDP) scheme for smart grid in a community environment, which enables the electricity price to correspond to the electricity usage in real time. In the UDP scheme, to simplify price management and reduce communication overhead, we introduce distributed community gateways as proxies of the utility company to timely respond to the price enquiries from the community customers. We consider both community-wide electricity usage and individual electricity usage as factors into price management: a customer gets higher electricity unit price if its own electricity usage becomes larger under certain conditions of the community-wide collective electricity usage. Additionally, we protect the privacy of the customers by restricting the disclosure of the individual electricity usage to the community gateways. Lastly, we provide privacy and performance analysis to demonstrate that the UDP scheme supports real-time dynamic pricing in an efficient and privacy-preserving manner.

In this paper, we study the optimal energy delivery problem from viewpoints of both the vehicle owner and aggregator, in load shaving services of a vehicle-to-grid (V2G) system. We formulate the optimization problem based on a general plug-in hybrid electric vehicle (PHEV) model, taking into account the randomness in vehicle mobility, time-of-use electricity pricing, and realistic battery modeling. Stochastic inventory theory is applied to analyze the problem. We mathematically prove that a state-dependent (S,S') policy is optimal for the daily energy cost minimization of each vehicle, and develop an estimation algorithm to calculate the parameters of the optimal policy for practical applications. Furthermore, we investigate the multi-vehicle aggregator design problem by considering the power system constraints. A policy adjustment scheme is proposed to adjust the values of S and S' with respect to the optimal policy adopted by each PHEV, such that the aggregated recharging and discharging power constraints of the power system can be satisfied, while minimizing the incremental cost (or revenue loss) of PHEV owners. Based on characteristics of the state-dependent (S,S') policy and our proposed policy adjustment scheme, the optimal aggregator operation problem is transformed into a convex optimization one which can be readily solved by existing algorithms. The performance of our proposed schemes is evaluated via simulations based on real data collected from Canadian utilities, households, and commuters.

The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.

As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.

Smart grid has emerged as the next generation of power grid, due to its reliability, flexibility, and efficiency. However, smart grid faces some critical security challenges such as the message injection attack and the replay attack. If these challenges cannot be properly addressed, an adversary can maliciously launch the injected or replayed message attacks to degrade the performance of smart grid. To cope with these challenging issues, in this paper, we propose an efficient authentication scheme that employs the Merkle hash tree technique to secure smart gird communication. Specifically, the proposed authentication scheme considers the smart meters with computation-constrained resources and puts the minimum computation overhead on them. Detailed security analysis indicates its security strength, namely, resilience to the replay attack, the message injection attack, the message analysis attack, and the message modification attack. In addition, extensive performance evaluation demonstrates its efficiency in terms of computation complexity and communication overhead.

Smart grid, as the next generation of power grid characterized by “two-way” communications, has been paid great attention to realizing green, reliable, and efficient electricity delivery for our future lives. In order to support the two-way communications in smart grid, a large number of smart meters (SMs) should be deployed to customers to report their near real-time data to control center for monitoring purpose. However, this kind of real-time report could disclose users’ privacy, bringing down the users’ willingness to participate in smart grid. In order to address the challenge, in this paper, by considering the lifetime of SMs as exponential distribution, we propose a diverse grouping-based aggregation protocol with error detection (DG-APED), which employs differential privacy technique into grouping-based private stream aggregation for secure smart grid communications. DG-APED can not only achieve privacy-preserving aggregation, but also perform error detection efficiently when some SMs are malfunctioning. Detailed security analysis shows that DG-APED can guarantee the security and privacy requirements of smart grid communications. In addition, extensive performance evaluation also verifies the effectiveness and efficiency of the proposed DG-APED.

Decentralized inverter control is essential in distributed generation (DG) microgrids for low deployment/operation cost and high reliability. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information of the total real and reactive power generation of all DG units. The information is acquired by a wireless network (such as a WiFi, ZigBee, and/or cellular communication network) in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of the proposed control strategy based on small-signal stability analysis. As timely communication may not be established for a microgrid with low-cost wireless communication devices, two kinds of analytical models are developed with respect to negligible and nonnegligible communication delays, respectively. Extensive numerical results are presented to demonstrate the system stability under the proposed control strategy with respect to different.