• Energy Research

This paper proposes a double-time-scale coordinated voltage control scheme for distribution networks with distributed generators (DGs) based on model predictive control (MPC) to regulate the voltage profile across a network. The slow-time-scale control scheme is designed to correct long-term voltage deviations while reducing the number of actions of the on-load tap changer, step voltage regulators, and capacitor banks. The MPC problem is formulated as a mixed-integer quadratic programming (MIQP). A tailored exaction solution method based on the branch-and-bound algorithm embedded with an alternating direction method of multiplier-based QP solver is developed to efficiently solve the MIQP problem. In the fast-time-scale control, the active and reactive power outputs of DGs are optimally coordinated to handle the fast voltage fluctuations as well as capture more renewable energy. An efficient analytical sensitivity calculation method is used to update the voltage sensitivities online. The effectiveness of the proposed control scheme along with the exact solution method is verified on a modified real 20 kV distribution system.

With the proliferation of microgrids (MGs) into the utility grid (UG), MGs with multiple distributed generations (DGs) face challenge to flexibly support voltage during UG faults. In this paper, a multi-stage voltage support optimization method is proposed to stabilize voltage, current and power, for safeguarding the MG operation and providing flexible power delivery capacity. The proposed method consists of three stages: 1) voltage stage in which the phase voltage instead of the sequence voltage regulation is considered to support voltage more precise; 2) current stage in which a novel current generation strategy with maximum current limiting (MCL) is formulated by directly embedding network impedance features; 3) power stage in which multiple matching scenarios for max/min active/reactive powers are developed to improve the flexibility of power delivery capacity. Several test cases are presented and pertinent results are discussed to validate the effectiveness of the proposed method for voltage support of MG with multiple DGs.

Centralized biogas plant (CBP) provides an attractive solution to the energy supply for district heating, electric loads, and residential cooking in remote areas via a local biogas delivery network. To overcome the challenges of biomass availability for CBPs, an integrated expansion planning model is proposed in this paper. The model makes investment decisions on the sequential planning of integrated electric power distribution and biogas delivery networks and uses the constrained biomass transportation network to optimally determine CBP locations in the integrated network. The proposed approach also considers the biomass availability and the cost of biomass supply in the integrated model. The proposed multistage stochastic planning problem makes investment decisions considering the gradual realization of uncertainties as loads grow and biomass is made available. The polyhedral approximation method is applied to convert the proposed mixed-integer second-order cone programming (MISOCP) problem to a mixed-integer linear programming (MILP) problem for making the problem more tractable for large-scale cases. The effectiveness of the proposed integrated model is validated using an 8-node and a 24-node test system.

This article proposes an optimal coordinated scheduling of electric vehicles (EVs) for a virtual power plant (VPP) considering communication reliability. Recent advancements on wireless technologies offer flexible communication solutions with wide coverage and low-cost deployment for smart grid. Nevertheless, the imperfect communication may deteriorate the monitoring and controlling performance of distributed energy resources. An interactive approach is presented for combined optimization of dynamic spectrum allocation and EV scheduling in the VPP to coordinate charging/discharging strategies of massive and dispersed EVs. In the proposed approach, a dynamic partitioning model of the multi-user multi-channel cognitive radio is used to cope with the vehicle-to-grid (V2G) communication issue due to variable EV parking behaviors, and a two-stage V2G dispatch scheme is proposed for the wind-solar-EV VPP to maximize its overall daily profit. Furthermore, the effects of packet loss probability on the VPP scheduling performance and battery degradation cost are thoroughly analyzed and investigated. Comparative studies have been implemented to demonstrate the superior performance of the proposed methodology under various imperfect communication conditions.

The utilization rate of power equipment plays a decisive role in the economic operation of power utilities. By determining the reasonable range of life cycle utilization rate of the distribution network equipment, it is of great significance to the management of the distribution network equipment. In this paper, the reasonable range of the life cycle utilization rate of distribution network equipment is determined. The life cycle utilization rate of distribution network equipment depends on the burden rate, load rate, and life expectancy rate, whose reasonable values are analyzed and exemplified, respectively. The optimal model of the burden rate in different conditions is established. The different load characteristic curves are also given by sorting out the load data. The calculation method of the life expectance rate is presented in this paper. The reasonable range of the life cycle utilization rate is finally obtained by defining the boundary condition of its composition. By setting the reasonable range of life cycle utilization rate of the distribution network equipment, power utilities can improve efficiency.

Abstract Differentiation of resilience from reliability has been a heated topic ever since the emergence of the former upon the limitation, and as a complement, of the latter, while finding the common ground for both has been scarce. This study first picks out the steady-state performance of both to be the common ground, while differentiating their typical causes, namely, the extreme weather for the resilience threats and the component ageing for the reliability challenges. An original evaluation framework developed earlier for the sole reliability is then extended here towards this common ground to accommodate resilience, becoming the joint reliability and resilience evaluation framework. The joint evaluation framework is built upon the Monte Carlo simulation, which embeds the rolling unit commitment as the system operation module to balance the optimality of operation strategy and the punctuality of condition update, and the forecast module with the machine learning technique for generate varied operation conditions. The proposed evaluation framework that joins resilience with reliability not only has its efficacy validated on the 39-bus system, but also deepens the evaluation by analyzing scenarios created with variating the values of key factors that impact resilience, including the typhoon speed, load factor, restoration time, and typhoon direction. Such a joint reliability and resilience evaluation pioneers as an integrated approach to conduct both evaluations in three aspects, namely, differentiating the causes of reliability and resilience harms and paralleling the challenges of both on system performance, upgrading the existing evaluation methods by applying the rolling mechanism to the operation strategy, and offering an open framework to embed complex functions, such as forecast tools enabled by machine learning and proactive responses for resilience enhancement.

This article proposes a distributed multi-period multi-energy operational model for the multi-carrier energy system. In this model, energy hubs function as distributed decision-makers and feature the synergistic interactions of generation, delivery, and consumption of coupled electrical, heating, and natural gas energy networks. The multi-period multi-energy scheduling is a challenging optimization problem due to its strong couplings and inherent nonconvexities within the multi-energy networks. The original problem is thus reformulated as a mixed integer second-order cone programming (MISOCP) and subsequently solved with a sequential second-order cone programming (SOCP) approach to guarantee a satisfactory convergence performance. Furthermore, a fully-distributed consensus-based alternating direction method of multipliers (ADMM) approach with only neighboring information exchange required is developed to optimize the multi-energy flows while considering the local energy-autonomy of heterogeneous energy hubs. The proposed methodology is performed and benchmarked on a four-hub urban multi-energy system over a 24 hourly scheduling periods. Simulation results demonstrated the superiority of the proposed scheme in system operational economy and renewable energy utilization, and also verify the effectiveness of the proposed distributed approach.