This paper proposes a data-driven distributionally robust chance constrained real-time dispatch (DRCC-RTD) considering renewable generation forecasting errors. The proposed DRCC-RTD model minimizes the expected quadratic cost function and guarantees that the two-sided chance constraints are satisfied for any distribution in the ambiguity set. The Wasserstein-distance-based ambiguity set, which is a family of distributions centered at an empirical distribution, is employed to hedge against data perturbations. By applying the reformulation linearization technique (RLT) to relax the quadratic constraints of the worst-case costs and constructing linear reformulations of the DRCCs, the proposed DRCC-RTD model is cast into a deterministic linear programming (LP) problem, which can be solved efficiently by off-the-shelf solvers. Case studies are carried out on a 6-bus system and the IEEE 118-bus system to validate the effectiveness and efficiency of the proposed approach.

There is a significant gap between academic research and practical application for power distribution system planning (PDSP). For most of the existing PDSP models in academic research, cost is used as the objective function, and the most common constraints are power flow equality constraints, bus voltage or voltage drop limits, substation and feeder capacity limits, etc. Although various advanced models and methods have been proposed, they are rarely used in real distribution system companies. This paper proposes a new feeder planning model for the urban distribution network considering various practical requirements. According to field investigation, "supply electricity to loads using nearby power sources" is used as the objective, and it is analytically expressed by load moment. Block loads, practical street layout, meshed network configuration, connection mode of feeders, non-crossing requirement of feeders, power supply radius, etc., are all respected and embedded in the model. The proposed model is recast as a mixed integer linear programming (MILP) problem, and it can be solved by the state-of-art commercial solver. The effectiveness and validity of the proposed model are illustrated on a 9-block test system and a real 22-block district distribution system.

The precision and reliability of distributed photovoltaic (PV) power forecasting play a crucial role in commercial PV plants for power integration and operation. However, the stochastic and intermittent nature of solar radiation affects forecasting accuracy. What's more, the diverse location and various meteorological information have brought challenges to real-time distributed forecasting. Therefore, it is more difficult for forecasting distributed power than centralized power. To solve this problem, we propose a four-stage space-time hybrid method for power forecasting of distributed PV plants. Firstly, Global and Local Feature Fusion Network (GLFFNet) is proposed to forecast the power output of centralized PV plants. Secondly, the weather clustering type is obtained to get ready for studying the relationship between centralized and distributed PV plants. Then, uncovered coefficients are figured out to show the distribution features of PV power both in the time and space domain. Finally, the correlation models are established based on Copula analysis. According to the above steps, the centralized forecasting results are transformed to obtain the distributed forecasting model. The case study shows that the proposed GLFFNet is more suitable for 1-hour ahead forecasting. Compared to 9 different neural models, MAPE reductions of GLFFNet model (time steps of 1 hour and 15 minutes) are 12.17%-23.23% and 3.18%-18.22%, respectively. Thus, MAPE of the case distributed PV power forecasting models are 0.62% (Type A), 0.96% (Type B) and 3.21% (Type C), respectively.

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two- way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this paper, we propose a privacy-preserving fair exchange scheme V2GEx for V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions cost only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.

With the rapid increase of renewable energy sources, the variability and uncertainty in power systems are significantly increased, and the power system operation is facing more and more challenges. In the traditional dynamic economic dispatch (DED) of transmission systems, the frequency and voltage regulation effects are not actively utilized. In addition, the transmission systems and distribution systems are optimized separately; the transmission–distribution coordination effect is not considered. In this article, a new DED is proposed. The optimal scheduling results are obtained by making a balance between operating cost and reliability cost. Both the automatic regulation effect and transmission–distribution coordination effect are explored and exploited in terms of the equivalent reserve, which can reduce the reserve provided by generating units, and can further enhance the security and economics of power system operation. The validity and effectiveness of the proposed model are illustrated by case studies.