• Energy Research

Primary distribution systems are usually simplified as fixed or flexible loads in the Independent System Operator (ISO) energy market clearing with favorable computational features. However, in emerging distribution systems with an increasing penetration of distributed energy resources (DER), this simplify-cation could easily fail to capture economic features of DERs and internal limits of distribution systems, triggering line congestion and under/over-voltage issues. To this end, a feasible region projection-based approach is proposed in this paper to optimally integrate high DER-penetrated distribution systems into the ISO energy market clearing, while effectively capturing configuration details of distribution systems and fully respecting their internal physical limits such as voltage and line flow constraints. Numerical studies show efficacy of the proposed approach in achieving the optimal integration of high DER-penetrated distribution systems into the ISO energy market, while: (i) not requiring ISO directly formulating full distribution systems with exhaustive variables and constraints; and (ii) not necessitating an iterative procedure to interact ISO with distribution systems, thus compatible to the current ISO market practice.

The emerging distribution system with a proliferation of distributed energy resources and flexible demand assets is expected to experience a restructuring process, just as what has been happening in the transmission system. This paper introduces the distribution locational marginal price (DLMP) as an effectively economic signal to quantify marginal cost for supplying next incremental loads at different phases of individual nodes. DLMP is calculated by solving the unbalanced ac optimal power flow (ACOPF) problem of distribution systems, with the objective of minimizing system operation cost. Indeed, in order to derive effective DLMPs, global optimal solution to the non-convex unbalanced ACOPF problem with a zero duality gap needs to be obtained. This paper solves the unbalanced ACOPF problem via the moment relaxation-based semidefinite programming (SDP) model. System sparsity is explored to accelerate the computational performance. In addition, a hierarchical approach is proposed to recover a good enough feasible solution to the original ACOPF, when the sparse moment relaxation-based SDP model is inexact. Numerical case studies on a modified IEEE 34-bus system evaluate the effectiveness and validity of the proposed approach. DLMP-based revenue adequacy of the distribution system is also analyzed.

Featured with fast response abilities and high ramp rates, energy storage systems (ESS), such as pumped-storage hydropower (PSH) plants and battery storage systems (BSS), are considered as key first-responders to provide spinning reserve in response to system contingencies. However, ESSs are energy-limited resources, and their sustained spinning reserve deployment is restricted by the stored energy and available capacity. Indeed, the spinning reserve deployment against contingencies may deviate actual state-of-charge (SOC) from the scheduled value, inducing potential SOC boundary violations and non-dispatchability in later hours. This paper proposes the post-contingency operation model and the spinning reserve secure constraints to address these issues. The post-contingency operation model describes that spinning reserve from ESSs is promptly deployed when contingency occurs, and then gradually substituted by quick-start units when they are switched online. By leveraging this operation strategy, spinning reserve secure constraints in terms of SOC headrooms are adopted to guarantee its deplorability. A modified IEEE 118-bus system with multiple PSHs and BSSs is used to verify the proposed approach.

Security‐constrained unit commitment (SCUC) is one of the most important optimisation problems for operation planning of power systems. Indeed, with the fast expansion of power grids and the increasing growth of heterogeneous market participants in electricity markets, the day‐ahead SCUC continuously encounters significant performance challenges. To improve the computational performance of SCUC and achieve solutions of good quality, an integrated framework which combines a data‐driven approach and a variable‐aggregation method is presented in this study. The variable‐aggregation method effectively approximates the original network security constraints with a reduced number of variables. Moreover, as aggregated constraints could reduce the feasible region of the original SCUC and potentially degrade solution quality for certain SCUC instances, it is preferable to only apply such an aggregation approach towards difficult SCUC instances. Therefore, a data‐driven classification method, by adopting relevant SCUC input data as features, is integrated to first predict whether a SCUC instance is ‘easy’ or ‘hard’. To this end, the integrated framework could improve the overall performance of SCUC instances, by and large, in terms of computational efficiency and solution quality. Numerical results illustrate the effectiveness of the proposed integrated framework.

An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input-output sequence characteristics. Then,an E-Seq2Seq approach with a multiple-encoder-decoder architecture and a fully connected extension layer is proposed. On this basis,the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct the deep learning model,and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.

Security of the smart grid is at risk when the vulnerabilities of the electric vehicle (EV) infrastructure is not addressed properly. As vehicles are becoming smarter and connected, their risk of being compromised is increasing. On various occasions and venues, hacking into smart or autonomous vehicles have been shown to be possible. For most of the time, a compromised vehicle poses a threat to the driver and other vehicles. On the other hand, when the vehicle is electric, the attack may spread to the power grid infrastructure starting from the EV supply equipment (EVSE) all the way up to the utility systems. Traditional isolation-based protection schemes do not work well in smart grid since electricity services have availability constraints and few of the components have physical backups. In this paper, we propose a mixed integer linear programming model that jointly optimizes security risk and equipment availability in the interdependent power and EV infrastructure. We adopt an epidemic attack model to mimic malware propagation. We assume malware spreads during EV charging when an EV is charged from an infected EVSE and then travels and recharges at another EVSE. In addition, it spreads through the communication network of EVSEs. The proposed response model aims to isolate a subset of compromised and likely compromised EVSEs. The response model minimizes the risk of attack propagation while providing a satisfactory level of equipment availability to supply demand. Our analysis shows the theoretical and practical bounds for the proposed response model in smart grid in the face of attacks to the EV infrastructure.