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Under future low-carbon scenarios, it will be crucial to boost the rapidity in the delivery of frequency response services, especially those provided by conventional generators. This paper proposes a novel framework for the scheduling of frequency response services that incentivizes a rapid response from different system assets, including conventional generators. Differently from other approaches which envisage a fixed delivery time for frequency response services, the proposed methodology implements time-varying delivery intervals, which coincide with the time of frequency nadir occurrence. A relevant set of linearized constraints is embedded in a typical power system scheduling model. Results indicate that the proposed methodology reduces the unnecessary overscheduling associated with traditional methodologies, leading to significant operational cost savings. [ABSTRACT FROM AUTHOR]

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

In order to reduce the dependence on accumulated experience that is difficult to replicate for the human dispatchers to make power generation scheduling more automatically, quickly, and intelligently, higher requirements are placed on the level of the auxiliary decision-making system. In this paper, a short-term scheduling problem was treated as a regression task that concentrates on how to learn a reliable statistic model that concludes the intrinsic logic of the dispatching policy from extensive historical dispatching experience. In this way, since Kullback-Leibler distance can better measure the distance between two distributions, we designed a novel GAN-based learning method for the scheduling task. Also, we proposed a feasible framework that combines the stage of learning, decision-making, and deployment to support the practical implementation of the proposed algorithm. In the experiment, a real case that takes short-term scheduling tasks on a regional large-scale power system is considered in our experiments. As a result, the comparison of several methods further shows the superiority of the proposed GAN-based method under our implementations. [ABSTRACT FROM AUTHOR]

Bulk integrated electricity and gas systems (IEGSs) introduce complex coupling relations and induce synergistic operation challenges. The growing uncertainty arising from the renewable power generation in the IEGS further aggravates the synergistic problems. Considering the availability of historical wind power generation data, this paper adopts a two-stage sample robust optimization (SRO) model, which is equivalent to the two-stage distributionally robust optimization (DRO) model with a ${\text{type}} - \infty $ Wasserstein ambiguity set, to address the wind power penetrated unit commitment optimal energy flow (UC-OEF) problem for the IEGS. Compared to the equivalent DRO model, the two-stage SRO model can be approximately transformed into a computationally efficient form. Specifically, we employ linear decision rules to simplify the proposed UC-OEF model. Moreover, we further enhance the tractability of the simplified model by exploring its structural features and, accordingly, develop a solution method. Simulation results on two IEGSs validate the effectiveness of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Temporal decomposition is a potential approach to relieve the computation cost of power system multi-interval scheduling problems, such as economic dispatch. In this form of decomposition, the considered scheduling horizon is partitioned into several subhorizons. A subproblem is formulated for each subhorizon, and a distributed optimization algorithm strategy is used to coordinate subproblems. The main existing challenge is decomposing the scheduling horizon to gain the most time saving from distributed computing. This paper serves as an extension to our previous work and presents a machine learning-aided temporal decomposition strategy to partition a scheduling horizon optimally. We have found that the load profile, known before solving economic dispatch, significantly affects the best number of subhorizons. We have used load profiles as inputs to a learner whose goal is to assign a temporal decomposition class to each load profile. Possible decomposition classes are divisors of the considered scheduling horizon. Thus, the proposed learning procedure is a multiclass classification. We have selected Extreme Gradient Boosting that is a tree-based classification learner. Simulation results using real-world load profiles show the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time coordinated scheduling method for active distribution networks (ADNs) with soft open points (SOPs) and plug-in electric vehicles (PEVs) via multi-timescale framework under uncertainties. Specifically, this method is achieved with day-ahead pre-scheduling and intra-day corrective control stages by coordinating various flexible resources at different timescales. The day-ahead stage is designed to reduce operational cost, regulate voltage profile and avoid risk exposure through joint scheduling of traditional devices, SOPs and PEVs on hourly basis. In intra-day corrective control stage, an hour is further divided into two timescales. The slow-timescale scheduling (STS) aims to corrective coordination of SOPs and across-time-and-space energy transmission of PEVs, and nested within the STS, the fast-timescale scheduling optimally coordinates the active & reactive power of SOPs and PV inverters to handle fast voltage fluctuations as well as against real-time uncertainties. The formulated three models are all transformed into second-order cone programming problems via sample weighted average approximation (SWAA), linearization and conic relaxation, which can be thus efficiently solved. Case studies based on three modified distribution systems (including two IEEE test systems and one actual distribution network) are performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders. [ABSTRACT FROM AUTHOR]

Market and network integration of distributed energy resources can be facilitated by their coordination within a virtual power plant (VPP). However, VPP operation subject to network limits and different market and physical uncertainties is a challenging task. This paper introduces a framework that co-optimizes the VPP provision of multiple market (e.g., energy, reserve), system (e.g., fast frequency response, inertia, upstream reactive power), and local network (e.g., voltage support) services with the aim of maximizing its revenue. To ensure problem tractability, while accommodating the uncertain nature of market prices, local demand, and renewable output and while operating within local network constraints, the framework is broken down into three sequentially coordinated optimization problems. Specifically, a scenario-based robust optimization for day-ahead resource scheduling, with linearized power flows, and two receding horizon optimizations for close-to-real-time dispatch, with a more accurate second-order cone relaxation of the power flows. The results from a real Australian case study demonstrate how the framework enables effective deployment of VPP flexibility to maximize its multi-service value stack, within an uncertain operating environment, and within technical limits. [ABSTRACT FROM AUTHOR]

To meet the diverse operational requirements of microgrids with multi-uncertainties, a tri-level two-stage mixed-integer robust optimization model is proposed to solve the optimal scheduling problem with source-load power and unit N-1 uncertainties. In the first stage, the optimal goal is to obtain robust scheduling plans that can be applied effectively in satisfying the operation and adjustment constraints, as well as to find the minimal operating cost in the basic scenario. Considering the system may concern a specific operating factor (e.g., load satisfaction rate) under a N-1 contingency, the second stage of the model minimizes the total energy of load shedding and renewable energy abandonment in the worst scenario. As the proposed robust model is a multi-level coupled mixed-integer optimization problem, which is difficult to address by either the duality method or the Karush–Kuhn–Tucker (KKT) method, a novel nested reformulation-and-decomposition (R&D) algorithm is proposed to handle this complex problem. Numerical simulations and tests have verified the effectiveness and superiority of the proposed robust model and the solution algorithm. Furthermore, the robust optimal schemes satisfy the engineering application more reasonably. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for advanced demand side management (DSM) in the distribution network (DN), catering at the same time for the requirements of the network operator, transmission or distribution, the available flexibility of the demand side, and the preservation of network performance. The basic premise of the study is that the distribution network operator is providing flexibility to the transmission system operator through load shaping. Different, one or more, indicators can be chosen to assess preservation of network performance. In this paper, for illustrative purposes of the methodology, the steady state voltage stability index, namely the load margin, is chosen as the network performance indicator. This is evaluated before and after a DSM action, in order to analyse the possible effect of DSM on network loadability. Load at each DN bus is represented using a realistic composite load model comprising controllable and uncontrollable loads. The DSM is initially performed based on optimal power flow calculation to ensure that the distribution system load profile at the grid supply point follows the required (pre-specified) load profile during a 24 hour period. Following this, a particle swarm optimisation is used to modify DSM program in those time steps of the planning horizon where load margin is reduced. The methodology is illustrated on a number of case studies using modified IEEE 33 bus distribution network model. The results illustrate the extent to which the available flexibility of demand can be used in support of transmission network operation when the preservation of network performance and appropriate load modeling are considered. [ABSTRACT FROM AUTHOR]

The paper focuses on the day-ahead operational planning of a grid-connected local energy community (LEC) consisting of an internal low-voltage network and several prosumers including generation units, battery storage systems, and local loads. In order to preserve, as much as possible, the confidentiality of the features of prosumers’ equipment and the production and load forecasts, the problem is addressed by designing a specific distributed procedure based on the alternating direction method of multipliers (ADMM). The distributed procedure calculates the scheduling of the available energy resources to limit the balancing action of the external grid and allocates the internal network losses to the various power transactions. Results obtained for various case studies are compared with those obtained by a centralized optimization approach. The results confirm that, in the considered LEC framework, each of the prosumers achieves a reduction in costs or increases revenues in case it participates to the LEC with respect to the case in which it can only transact with an external energy provider. [ABSTRACT FROM AUTHOR]

Networked microgrids (MGs) are considered as an emerging grid design for the future distribution system (DS). The coordination of the networked MGs is critical in order to further enhance the operation efficiency and reliability of the system. In this paper, a transactive energy (TE) framework is proposed for the coordinated energy management of networked MGs in DS. Instead of direct coordination signals and fixed pricing schemes, the distribution network operator (DNO) organizes a transactive market with the MGs to coordinate the energy management in the operation. Further, a distributionally robust optimization (DRO)-based algorithm is developed to provide a robust solution of the detailed scheduling decisions in the proposed TE framework under uncertainty without being too conservative. Case studies with the proposed framework were conducted with the IEEE 33-bus system with three MGs and the IEEE 123-bus system with nine MGs. The results of the case studies show that the proposed TE-based framework can effectively coordinate the energy scheduling of the MGs. The operational cost of the DS is reduced significantly. Meanwhile, the proposed DRO-based algorithm provides a robust but not over-conservative solution for the operation decisions of the DNO and MGs in the proposed framework. [ABSTRACT FROM AUTHOR]

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a data-driven distributed chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates. All our code (Matlab) is publicly available at https://github.com/galdl/outage_scheduling. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a distributed electric vehicle (EV) coordination mechanism, which enables the management of the charging/discharging operation of an EV fleet, offering V2G regulation services to support system operation based on a day-ahead scheduling profile generated by an EV aggregator. Initially, a decentralized iterative algorithm is introduced to manage EV charging/discharging schedule aiming to meet the daily mobility energy requirement of an EV fleet with respect to the day-ahead schedule of the EV aggregator. Then, a droop-based control and a novel distributed regulation dispatch algorithm are developed aiming to meet the regulation dispatch signals send to the EV aggregator by the system. [ABSTRACT FROM PUBLISHER]

Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper investigates joint scheduling problem of large-scale smart appliances and batteries (e.g., in a smart building), to minimize electricity payment, user's dissatisfaction and battery loss under kinds of constraints. Due to the binary nature of charge and discharge states of battery, this problem is formulated as a constrained mixed-integer nonlinear program. In order to solve it efficiently, a distributed mixed optimization approach is proposed. First, Lagrangian relaxation is applied to decompose the original problem into two sets of subproblems, each of which corresponds to scheduling on appliance/battery. Then, the battery scheduling subproblem is formulated as a mixed-integer linear program and tackled by Benders decomposition. The main advantages of the proposed approach are the distributed implementation and low computational complexity, as shown by simulations. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed the significant growth in electricity consumption. The emerging smart grid aims to address the ever-increasing load through appropriate scheduling, i.e., to shift the energy demand from peak to off-peak periods by pricing tariffs as incentives. Under the real-time pricing environment, due to the uncertainty of future prices, load scheduling is formulated as an optimization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, we propose dual decomposition and stochastic gradient to solve the problem. That is, the primal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, we propose an online approach to further alleviate the impact of price prediction error. Numerical results are provided to validate our theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents an adjustable robust optimization approach to account for the uncertainty of renewable energy sources (RESs) in optimal power flow (OPF). It proposes an affinely adjustable robust OPF formulation where the base-point generation is calculated to serve the forecast load which is not balanced by RESs, and the generation control through participation factors ensures a feasible solution for all realizations of RES output within a prescribed uncertainty set. The adjustable robust OPF framework is solved using quadratic programming with successive constraint enforcement and can coordinate the computation of both the base-point generation and participation factors. Numerical results on standard test networks reveal a relatively small increase in the expected operational cost as the uncertainty level increases. In addition, solutions of networks that include both uncertain wind generation and Gaussian distributed demand are shown to have less cost and a higher level of robustness as compared to those from a recent robust scheduling method. [ABSTRACT FROM PUBLISHER]

We propose a probabilistic framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model. [ABSTRACT FROM PUBLISHER]

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon. [ABSTRACT FROM PUBLISHER]

The paper presents a new approach to the reinforcement scheduling problem in transmission power systems planning. This problem is typically formulated as a large scale multistage decision under uncertainty problem. We represent uncertainty by load/generation scenarios and propose the use of Gaussian search techniques to solve the scenario deterministic subproblems. The solution of each subproblem is a schedule of reinforcements. As information is different from scenario to scenario, scheduling conflicts may appear at optimal subproblem decisions. We solve these conflicts with a progressive hedging algorithm. Decision convergence on reinforcement scheduling is achieved for the first decision stage by progressively modifying the individual scenario subproblems. We present a rule for modifying scenario subproblems, discuss the algorithms presented, and illustrate their application with an example built over the IEEE 14-bus network. [ABSTRACT FROM AUTHOR]