Stochastic Optimization of an Active Network Management Scheme for a DER-Rich Distribution Network Comprising Various Aggregators

With large-scale acceptance of solar and wind energy generation into electric grids, large energy storage is expected to provide sufficient flexibility for the safe, stable and economic operation of power systems under uncertainty. Active Network Management (ANM) allows this to happen without having to enlarge the system. This paper presents an ANM-based cost minimization and curtailment model for day-ahead operational planning of active distribution systems. Electric Vehicles (EVs) are managed by EV Aggregators for profit purposes under different parking characteristics in the Vehicle-to-grid mode. A pricing mechanism that defines interaction between the Distribution System Operator (DSO) and EV Aggregators is proposed. Uncertainty terms involve the wind power outputs, solar power outputs and the power demand. The stochastic optimization model created 27 scenarios and solved the minimization problem which involves the grid supply point power, the non-firm power and the aggregator power. This is applied to IEEE-33 bus system and implemented in AIMMS. Results show how the impact of various aggregators’ availability profiles help to reduce network operating cost and curtailment of non-firm DGs and improve voltage profiles

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

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 distributed scenario-based 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

Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) becomes a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. Advanced ANM strategies imply that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. For example, decisions taken at a given moment constrain the future decisions that can be taken and uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. We first formulate the ANM problem, which in addition to be sequential and uncertain, has a nonlinear nature stemming from the power flow equations and a discrete nature arising from the activation of power modulation signals. This ANM problem is then cast as a stochastic mixed-integer nonlinear program, as well as second-order cone and linear counterparts, for which we provide quantitative results using state of the art solvers and perform a sensitivity analysis over the size of the system, the amount of available flexibility, and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available at http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution networks of 5, 33, and 77 buses. These test beds contain a simulator of the distribution system, with stochastic models for the generation and consumption devices, and callbacks to implement and test various ANM strategies

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Robust Optimization for SCED in AC-HVDC Power Systems

Wind power is a clean, renewable and low-carbon resource for power generation that has received increasing attention in power systems over the last few decades. There are two main challenges associated with the large-scale integration of wind power plants in the power system: i) the intermittent nature of wind power results in prediction errors that can greatly impact the system's operational security and reliability requirements, and ii) large-scale offshore wind farms are typically located far from onshore loads and require new developments in the transmission system of power grids, e.g., realization of mixed alternating current-high voltage direct current (AC-HVDC) power systems, which will introduce new reliability requirements to the system operator. The security-constrained economic dispatch (SCED) problem deals with determining a power dispatch schedule, for all generating units, that minimizes the total operational cost, while taking into account system reliability requirements. Robust optimization (RO) has recently been used to tackle wind power uncertainty in the SCED problem. In the literature of RO, the budget of uncertainty was proposed to adjust the solution conservatism (robustness) such that higher budgets of uncertainty correspond to more conservative solutions. This thesis shows that the budget of uncertainty approach may not be meaningful for problems with RHS uncertainty since increasing the budget of uncertainty by more than a certain threshold may not always impact the level of conservatism. This thesis proposes a new tractable two-stage robust optimization model that effectively incorporates the budget of uncertainty in problems with RHS uncertainty, controls the level of conservatism, and provides meaningful insights on the trade-off between robustness and cost. Furthermore, this thesis examines the applicability of the proposed robust approach for the SCED problem in mixed AC-HVDC power systems with large integration of wind power. The proposed robust SCED model considers the impact of wind power curtailment on the operational cost and reliability requirements of the system. Extensive numerical studies are provided to demonstrate the economic and operational advantages of the proposed robust SCED model in mixed AC-HVDC systems from five aspects: the effectiveness of the budget of uncertainty, robustness against uncertainty, contribution to real-time reliability, cost efficiency, and power transfer controllability

Reliability-Constrained Economic Dispatch with Analytical Formulation of Operational Risk Evaluation

Operational reliability and the decision-making process of economic dispatch (ED) are closely related and important for power system operation. Consideration of reliability indices and reliability constraints together in the operation problem is very challenging due to the problem size and tight reliability constraints. In this paper, a comprehensive reliability-constrained economic dispatch model with analytical formulation of operational risk evaluation (RCED-AF) is proposed to tackle the operational risk problem of power systems. An operational reliability evaluation model considering the ED decision is designed to accurately assess the system behavior. A computation scheme is also developed to achieve efficient update of risk indices for each ED decision by approximating the reliability evaluation procedure with an analytical polynomial function. The RCED-AF model can be constructed with decision-dependent reliability constraints expressed by the sparse polynomial chaos expansion. Case studies demonstrate that the proposed RCED-AF model is effective and accurate in the optimization of the reliability and the cost for day-ahead economic dispatch

A robust energy and reserve dispatch model for prosumer microgrids incorporating demand response aggregators

Abstract: The uncertainty introduced by intermittent renewable energy generation and prosumer energy imports makes operational planning of renewable energy‐assisted prosumer microgrids challenging. This is due to the difficulty in obtaining accurate forecasts of energy expected from these renewable energy sources and prosumers. Operators of such microgrids therefore require additional grid‐balancing tools to maintain power supply and demand balance during grid operation. In this paper, the impact of demand response aggregators (DRA’s) in a prosumer microgrid is investigated. This is achieved by developing and solving a deterministic mathematical formulation for the operational planning of the grid. Also, taking a cue from CAISO’s proposed tariff revision which allows the state‐of‐charge of non‐generator resources (like storage units) to be submitted as a bid parameter in the day‐ahead market and permits scheduling coordinators of these resources to self‐manage their energy limits and state‐of‐charge, the proposed formulation permits prosumers to submit battery energy content as a bid parameter and self‐manage their battery energy limits. Furthermore, a robust counterpart of the model is developed. Both formulations are constrained mixed integer optimization problems which are solved using the CPLEX solver in Advanced Interactive Multidimensional Modelling System (AIMMS) environment. Results obtained from tests carried out on a hypothetical prosumer microgrid show that the operating cost of the microgrid reduces in the presence of DRA’s. In addition, the storage facility owner may benefit from self‐managing its energy limits, but this may cut the amount of grid‐balancing resource available to the microgrid operator, thereby increasing the operating cost of the microgrid

Impact of Forecast Errors on Expansion Planning of Power Systems with a Renewables Target

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a stochastic programming modeling framework to determine the expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study