Power System Constraints Research Articles

An Integrated Analysis of Technical and Economic Problems to Support Power System Constraints with a Highly Dependence of Thermal Power Plants

In a centralized wholesale power system market scenario, the transmission and distribution constraints limit dispatch and require additional generation to support an integrated area known as Security Generation. This one must be covered in each area, and its cost transferred to the final users. The security generation cost gets higher when operation implicates the use of thermal power plants whose price equation depends on international fuels costs. This paper examines the consequences of a high reliance on thermal power plants. It focuses on scheduled reserves affecting electricity unit costs and the potential for constraints to lead to long-term consequences. The paper analyzes the security generation behavior based on reports and uses a real scenario to support simulations and decisions, evidencing monthly cost and an estimated CO2 emission. Results show the direct cost-saving potential of investing in renewable projects and technology. The paper can allow to replicate the analysis in comparable areas and regions with similar challenges.

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

A wind farm planning method considering frequency security constraint based on wind farm participation in primary frequency regulation

Abstract Renewable energy sources such as wind power, connected to the grid on a large scale, reduce the rotational inertia and frequency regulation of the power system. Many studies have included frequency security constraints (FSC) in power system scheduling to ensure system frequency stability, but few studies have included FSC in grid planning. Therefore, this paper establishes a planning model for wind farms that considers the FSC based on the wind turbines’ participation in the primary frequency regulation (PFR) by obtaining the maximum deviation value constraint of the system frequency through the system frequency response (SFR) model. Tests were conducted on the IEE30-node system, and the results show that the proposed model enhances the system frequency response capability and ensures system security.

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with non-dominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME

Quantifying the impact of multi-scale climate variability on electricity prices in a renewable-dominated power grid

The variability and intermittency of renewable energy sources pose several challenges for power systems operations, including energy curtailment and price volatility. In power systems with considerable renewable sources, co-variability in renewable energy supply and electricity load can intensify these outcomes. In this study, we examine the impacts of interplay between renewable resource and load co-variability and power system constraints across multiple spatial and temporal scales. We use the New York State power system as our case study, which is undergoing a major transition toward increased renewable generation. We characterize the spatiotemporal co-variability of renewable energy-generating resources and electricity load and investigate the impact of climatic variability on electricity price volatility. We use an accurate, reduced-form representation of the New York power system, which integrates additional wind and solar power resources to meet the state’s energy targets through 2030. Our study shows while diversifying renewables mitigates power variability, short-term fluctuations can still lead to inefficient energy use and substantial curtailment. Annual electricity prices varied by 9%, with price spikes fluctuating by up to 56%, while reflecting a state-wide heterogeneity. Hydropower primarily influences yearly prices, whereas solar and wind mainly affect short-term volatility. Our results underscore the importance of considering both spatiotemporal dynamics and system constraints when planning investments in renewable energy infrastructure.

Optimal Sizing and Locations of Fast Charging Stations for Electric Vehicles Considering Power System Constraints

Optimal Sizing and Locations of Fast Charging Stations for Electric Vehicles Considering Power System Constraints

Unit Commitment with Joint Chance Constraints in Multi-area Power Systems with Wind Power Based on Partial Sample Average Approximation

Unit Commitment with Joint Chance Constraints in Multi-area Power Systems with Wind Power Based on Partial Sample Average Approximation

Energy Management System for Smart Grid in the Presence of Energy Storage and Photovoltaic Systems

Today, the desire to use renewable energy as a source of clean and available energy in the grid has increased. Due to the unpredictable behavior of renewable resources, it is necessary to use energy storage resources in the microgrid structure. The power generation source and the storage source in microgrids should be selected in such a way that it has the ability to respond to the maximum demand in the state connected to the grid and operate independently. In this article, the optimal capacity and economic performance of a microgrid based on photovoltaic and battery system have been investigated. In this way, first, using the iterative optimization method, the optimal microgrid capacity has been obtained. Then, the dynamic planning method has been used for optimal microgrid energy management. The simulation results show the accuracy and efficiency of the proposed solutions. The proposed controller, while automatically and dynamically adapting to the solar cell output changes, is capable of responding to external requests, such as price signals or satisfying power system constraints or operator requests. In addition, the results indicate that by using the proposed energy management system, the microgrid system can regain stability during one to two cycles, during the occurrence of PV system radiation changes as well as ESS charge changes. And also, according to the ESS charge changes, the voltage changes should be within the defined permissible range between 0.95 and 1.05 pu, which is the result of the unique efficiency of the proposed energy management system.

Optimal mitigation and control over power system dynamics for stochastic grid resilience

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the western system coordinating council 9-bus test system using synthetic multi-device outage scenarios.

A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.

Segmentation of interconnected power systems considering microgrids and the uncertainty of renewable energy sources

AbstractThis paper proposes an approach for segmentation of power systems to prevent uncontrolled islanding. With the development of microgrids (MGs) and increased penetration of renewable energy sources (RESs) in the power system, it is necessary to consider the effect of MGs and the stochastic behaviour of RESs in the controlled islanding process. First, the enormous search space for finding the boundary points is reduced by the clustering of the coherent generators and the buses which are placed in sections that cannot be separated from the coherent groups. Then, linear AC power flow is defined as a sub‐problem and solved by linear programming to satisfy the power system and MG constraints. In this step, an optimization model is defined, and a mixed‐integer linear programming (MILP)‐based model is suggested to find cut‐sets and islands arrangement with reduced load shedding. In view of the fact that the fluctuation of RESs may have the significant effects on the power imbalance, to cover the uncertainties a scenario based stochastic simulation is considered. Finally, the effect of uncertainties on cut‐sets and load curtailment is evaluated. The effectiveness of the proposed methodology is demonstrated by the simulation on the test system.

Transmission Expansion Planning using Binary Differential Evolution considering forecasted solar Photovoltaic generation

Transmission Expansion Planning (TEP) can be essentially affected by solar Photovoltaic (PV) penetration because its generation is uncertain depending on weather and the environment at a specific time. This uncertainty can impact transmission expansion planning because the uncertain generation of solar PV causes difficulty to expand transmission lines with secure system operations. The aim of the proposed TEP method is to construct transmission lines to handle the increase in solar PV sources and electricity demand while ensuring that the power system constraints are not violated. The goal of the objective function is to minimize the total cost of the system, which comprises both the investment cost and the operating cost of generation. Hourly resolution values of solar PV generation and electricity demand are considered in the planning. Moreover, to obtain accurate values of solar PV generation for planning a system with secure operations, this paper proposes a Long Short-Term Memory (LSTM) for forecasting solar PV generation. The Binary Differential Evolution (BDE) algorithm is applied to solve the TEP problem. The proposed TEP method is tested through an IEEE 24 bus test system. The results from the proposed method show that the obtained expansion plan provides the minimum total system cost without system violations.

Transmission expansion planning considering solar photovoltaic using novel binary particle swarm optimization

Due to the demand growth and an increase in uncertain generation from solar photovoltaics which are promoted instead of fossil fuel, the transmission expansion planner needs to take this uncertain generation into account. This paper proposes Transmission Expansion Planning (TEP) considering solar PhotoVoltaic (PV) using Novel Binary Particle Swarm Optimization (NBPSO) algorithm to define the location and number of the Transmission Lines Candidate (TLC) that will be installed into the existing power system. The goal is to minimize the total cost consisting of the investment cost of the newly installed TL and the operation cost of power generation. The case study consists of two cases. The TEP when solar PV is disregarded is solved and analyzed in the first case. For the second case, the TEP when considering solar PV generation is studied. The aforementioned cases were analyzed on the IEEE 24 bus test system. The optimization program is written by MATLAB software with the MATPOWER tool to solve optimal power flow. From the simulation result, the NBPSO can find the TEP solution with the lowest investment cost and operating cost while the obtained results do not violate the power system constraints.

Community Resilience Optimization Subject to Power Flow Constraints in Cyber-Physical-Social Systems

This article develops a community resilience optimization method subject to power flow constraints in the cyber-physical-social systems in power engineering, which is solved using a multiagent-based algorithm. The tool that makes the nexus between the electric generation on the physical side and consumers and prosumers on the social side is the power flow algorithm. Specifically, the levels of emotion, empathy, cooperation, and the physical health of the consumers, prosumers are modeled in the proposed community resilience optimization approach while accounting for the electric power system constraints and their impact on the critical loads, which include hospitals, shelters, and gas stations, to name a few. The optimization accounts for the fact that the level of satisfaction of the society, the living standards, and the social well-being are depended on the supply of energy, including electricity. Evidently, the lack of electric energy resulting from load shedding has an impact on both the mental and the psychical quality of life, which in turn affects the community resilience. The developed constrained community resilience optimization method is applied to two case studies, including a two-area 6-buses system and a modified IEEE RTS 24-bus system.

Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF

The decarbonization of energy systems has posed unprecedented challenges in system complexity and operational uncertainty that render it imperative to exploit cutting-edge artificial intelligence (AI) technologies to realize real-time, autonomous power system operation and control. In particular, deep reinforcement learning (DRL)-based approaches in power systems are extensively studied and implemented in several trials worldwide. Nevertheless, the vulnerability of DRL brings new security threats to power systems that have not been well identified and investigated in the literature. To this end, this paper proposes a physics-constrained vulnerability assessment methodological framework for the DRL-based power system operation and control, with a special focus on the problem of security-constrained optimal power flow (SCOPF). In particular, we develop a novel adversarial example generation method, defined as a false data injection attack against the DRL-based SCOPF (FDIAI), to realize a targeted adversarial attack considering the nonlinear physical constraints in power systems via two main stages of constructor function design and unconstrained optimization problem transformation. In this way, the proposed FDIAI can significantly influence the decision-making procedure of DRL while successfully evading the bad data detection mechanism in power systems. Case studies are conducted to explore the stealthiness and effectiveness of FDIAI and then show its severe impacts on system operation and control on the winners' models of the Learning to Run a Power Network (L2RPN) competitions, including L2RPN IJCNN 2019 (IJCNN), L2RPN WCCI 2020 (WCCI), and L2RPN NeurIPS 2020 (NeurIPS).

Real-Time Allocation of Multi-Mobile Resources in Integrated Distribution and Transportation Systems for Resilient Electrical Grid

This article proposes a novel real-time optimization-based critical load restoration strategy for the post-event stages. The key idea is to integrate multi-mobile and stationary resources into the emergency tools of the system operation as grid-support services. The role of the demand response (DR) program and distributed generation (DG) from the resiliency-boosting points of view is investigated. To prevent prolonged and extensive electric outages, mobile emergency generator (MEG), emergency vehicle energy storage (EVES), and electric vehicles (EVs) are aimed to be exploited by forming microgrids. Power system constraints are modelled according to the linearized DistFlow equations. The framework's main aim is to serve critical loads, weighted by their priority, with the coordinated operation of multiple resources. Mixed-integer quadratic programming-based model is solved by a state-of-the-art solver within 30-minute time slots while considering uncertainties in real-time traffic congestion. Different case studies are carried out on the IEEE-33 bus coupled distribution-transportation system and the results are discussed in detail on the load restoration basis.

Risk-Based Operation Coordination of Water Desalination and Renewable-Rich Power Systems

Handling the variability and uncertainty associated with integrating large capacities of renewable energy sources (RES) into the power grid is a challenge that is increasingly influencing the power systems operation. At the same time, the growing need for desalinated water in arid areas increases the importance of suitable energy sources for sustainable operation of water desalination plants. However, as power and water system operators have traditionally operated their systems in isolation, there is a lack of understanding of the interdependence and interactions between these two systems. This paper addresses this gap by proposing a risk-based two-stage stochastic co-optimization framework that coordinates the operation of a renewable-rich power system with the operation of grid-connected reverse-osmosis water desalination plants (RO-WDP) to minimize their combined operational costs while increasing the utilization of RES. From the power system operation standpoint, the RO-WDPs are considered as controllable demand, and the proposed model integrates the energy flexibility of RO-WDPs in the day-ahead power system operation. The proposed model considers the operational constraints of both power and water desalination systems, thus co-optimizing their operation without compromising the reliable supply of power and water to end-users, while taking into account the uncertainty of the demands and RES. Simulation results demonstrate the benefits of the proposed coordination on enhancing the power system efficiency, facilitating RES integration, and minimizing the combined operational costs of both systems while minimizing their operating risk using conditional value at risk.

Optimal Motion of a Rolling Stock Fleet Under Traction Power System Constraints

The operators of the railway industry [i.e., traction system operator (TSO) and railway system operator (RSO)] are currently involved in a major process of energy transition. Aimed at improving both environmental sustainability and energy efficiency, technological infrastructures are being updated and new operating solutions are studied. Moreover, the business opportunities offered by the new rail market are forcing operators to search for power quality solutions. In this article, an efficiency strategy is proposed for optimally moving a fleet of trains while taking power quality indices into account. A method to relate the traction power requirements to the electrical variables of the railway infrastructure is formulated in terms of the power flow approach. Our numerical simulations focus on the real case of Metro Line 1 in the southern Italian city of Naples. The energy problem of moving the trains on the track is solved by including a set of constraints at the point of common interaction between railway market operators. Specifically, repetitive train starts and their speed profiles are optimally managed in terms of power requirements at traction power substations (TPSs) using power quality constraints. This strategy allows improvements in energy efficiency without compromising the quality of the railway service.

CoRE: Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Dear Editor, Machine learning (ML) approaches have been widely employed to enable real-time ML-based stability assessment (MLSA) of large-scale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems. In this letter, we propose a novel evaluation framework to analyze the robustness of MLSA against adversarial samples with key considerations for damage (i.e., the ability of the adversarial data to cause ML misclassification), bad data detection, physical consistency, and limited attacker's capacity to corrupt data. Extensive experiments are conducted to evaluate the robustness of MLSA under different settings.

Re-thinking var planning to mitigate reactive power reserves scarcity during the energy transition

The progressive replacement of conventional fossil-fueled power plants by renewable energy sources (RES) during the energy transition to renewable-dominated power supply leads to reactive power reserves (RPRs) scarcity, compromising thereby power systems security. This paper envisages the next step after having identified RPRs' scarcity with a methodology proposed previously by the authors. This paper re-thinks reactive power (or var) planning in the new context of mitigating RPRs' scarcity. Specifically, the paper proposes a new methodology and problem formulation of var planning that includes as main novelties: consideration of scenarios modeling RES uncertainty, temporal aspects (e.g. 24 hours operation profiles), accurate reactive power capability model for wind power plant (WPP) collection system, the precise modeling of the conventional generators' reactive power capability curves, and the joint consideration of both dynamic and static reactive power assets. The proposed var planning problem is formulated as a multi-period stochastic security-constrained optimal power flow (SCOPF), which seeks to minimize the investment cost in new reactive power assets while meeting power system constraints under various operating conditions. The value of the proposed methodology is briefly exemplified using a 60-nodes model of the Nordic32 system.