Power System Optimization Research Articles

T-spherical fuzzy interactive Dubois–Prade information aggregation approach for evaluating low-carbon technology impact and environmental mitigation

The concept of T-spherical fuzzy sets (T-SFSs) provides a robust optimization approach to deal with uncertainties in environmental concerns. The main objective of this paper is to introduce a hybrid approach for the power system reforms and advances of low-carbon technology to address environmental concerns as well as transition towards a more sustainable energy infrastructure. To meet these objectives, various aggregation operators (AOs) are developed named as T-spherical fuzzy Dubois–Prade (T-SFDP), T-spherical fuzzy interactive Dubois–Prade weighted average (T-SFIDPWA), T-spherical fuzzy interactive Dubois–Prade ordered weighted average (T-SFIDPOWA), T-spherical fuzzy interactive Dubois–Prade weighted geometric (T-SFIDPWG), and T-spherical fuzzy interactive Dubois–Prade ordered weighted geometric (T-SFIDPOWG). These AOs are integrated in the construction CRITIC-EDAS framework, facilitating a comprehensive assessment and ranking of proposals. This paper underscores the methodology's efficacy and precision through rigorous validation with CRITIC-MAUT, CRITIC-ARAS, CRITIC-EDAS, and CRITIC-MABAC analyses, showcasing its ability to discern and prioritize reform initiatives with exactitude. Beyond enhancing the comprehension of reform proposals, this hybrid approach establishes a benchmark for future evaluations, significantly contributing to the dynamic landscape of power system optimization. The methodology's clarity and precision offer decision-makers a valuable tool for the thorough assessment and prioritization of power system reforms initiatives, fostering sustainability and environmentally conscious advancements.

Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

In recent years, growing interest has emerged in investigating the integration of energy storage and green hydrogen production systems with renewable energy generators. These integrated systems address uncertainties related to renewable resource availability and electricity prices, mitigating profit loss caused by forecasting errors. This paper focuses on the operation of a hybrid farm (HF), combining an alkaline electrolyzer (AEL) and a battery energy storage system (BESS) with a wind turbine to form a comprehensive HF. The HF operates in both hydrogen and day-ahead electricity markets. A linear mathematical model is proposed to optimize energy management, considering electrolyzer operation at partial loads and accounting for degradation costs while maintaining a straightforward formulation for power system optimization. Day-ahead market scheduling and real-time operation are formulated as a progressive mixed-integer linear program (MILP), extended to address uncertainties in wind speed and electricity prices through a two-stage stochastic optimization model. A bootstrap sampling strategy is introduced to enhance the stochastic model’s performance using the same sampled data. Results demonstrate how the strategies outperform traditional Monte Carlo and deterministic approaches in handling uncertainties, increasing profits up to 4% per year. Additionally, a simulation framework has been developed for validating this approach and conducting different case studies.

Revolutionizing Power Systems Through Electrical Automation: Efficiency, Reliability, and Sustainability

This paper explores the transformative role of electrical automation technologies in the modernization and optimization of power systems. With a comprehensive analysis spanning power generation, distribution, and grid management, we delineate the multifaceted impacts of automation on enhancing operational efficiency, ensuring reliability, and promoting environmental sustainability. Through the lens of operational case studies and theoretical frameworks, we identify key insights into how electrical automation contributes to the streamlining of processes, reduction of human error, and integration of renewable energy sources. Moreover, we delve into the future trajectory of electrical automation, highlighting the potential integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the expansion of smart grid capabilities, and the paramount importance of cybersecurity in the digital era of power systems. Our discussion synthesizes these perspectives to underscore the pivotal role of electrical automation in driving the evolution of power systems towards greater adaptability, efficiency, and resilience. This paper posits that the future of power systems hinges on the strategic implementation of advanced automation technologies, guided by an overarching commitment to innovation, security, and sustainability.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

Optimal management of microgrid energy by considering demand side management plan and maintenance cost with developed particle swarm algorithm

This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive.

Long-term Load Forecasting for Optimal Power System Planning and Decision-Making

Forecasting the future load growth of an area based on its load demand is often a proactive measure to ensure a steady electricity power supply to that area. The study focused on long-term load forecasting for power system planning, specifically examining the electric load demand from consumers on distribution transformers within Port Harcourt City, located in Rivers State, Nigeria. The study encompassed a comprehensive review of both statistical and artificial intelligence-based approaches. Historical load data for distribution transformer readings spanning 2008 to 2017 were acquired from the Port Harcourt Electricity Distribution Company (PHEDC) and subjected to analysis using the curve-fitting technique. For the period between 2015 and 2030, a yearly load forecast simulation was conducted using the Fourier Series model, implemented with MATLAB software. This simulation aimed to provide insights into future load demand, facilitating careful and informed decision-making in the investment, operation, and maintenance of power system equipment. The effectiveness of the forecasting investigation was assessed using the Root Mean Square Error (RMSE), confirming the efficiency, reliability, and validity of the employed model. The study's forecasted results are presented as a valuable guide and practical tool for policymakers and the utility company (PHEDC) to enhance proper planning and decision-making processes. Considering the observed trend in the results, it is suggested that installing additional transformer units in the region would be necessary to alleviate the loads on existing overloaded transformer units within the power system network.

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

Assessing the carbon emission reduction effect of flexibility option for integrating variable renewable energy

Integrating a high-penetration level of variable renewable energy into power systems requires the pro-rata deployment of flexibility technology in the recent trends of low-carbon transition. This study proposes a multi-regional power system optimization model to assess the carbon emission reduction effect of different flexibility options in the Chinese power sector, including total carbon emissions, peaking timetables and values, carbon emission transfer between regional power systems, and carbon emission source changes between technologies. The results reveal that the combinations of dispatchable generation, inter-regional transmission, energy storage, and demand-side response can significantly reduce carbon emissions from power systems, but will lead to higher expenditure. Carbon emissions would peak 1–6 years in advance under the integrated flexibility option consisting of all flexibility technologies, and the corresponding peaking value would decrease by 0.26–0.48 billion tonnes compared to other configuration scenarios. However, this does not apply to all the regions. The coupling relationship between the flexibility option and carbon emission reduction in regional power systems changes with the disparities in power demand, system structure, and renewable energy consumption targets.

Analysis of multi-objective cooperative optimization strategy for residential load under extreme weather

In recent years, the problem of seasonal power supply shortages in extreme weather has frequently occurred across China, which has brought great inconvenience to people’s production and life. At the same time, with the advancement of the energy revolution, the proportion of electric energy in the terminal energy is increasing, which also poses a severe challenge to China’s energy security. Our research, by constructing a residential electricity consumption model and conducting multiple electricity price response experiments, has collected data that assesses various factors involved in user responses. It has also explored and inferred the impact of these factors on user-side responses. This study introduces new research directions aimed at alleviating electricity supply-demand imbalances during different periods, contributing to the optimization and enhancement of the national power system.

Optimal configuration and power scheduling algorithm of distributed power system with large range power flow transfer

The distributed power system is composed of multiple distributed energy sources, loads and energy storage devices, which have the characteristics of decentralization and complexity. Therefore, a distributed power system optimal allocation and power dispatching algorithm considering large-scale power flow transfer is proposed. Calculate the power flow values of different nodes, and construct an optimization model according to the results to complete power flow transmission. In the inner loop, the current dispatching is set as the objective, and in the outer loop, the voltage dispatching is set as the objective. Distributed power system scheduling is completed by double closed-loop vectors. The experimental results show that the power oscillation amplitude of this method is small and the duration is short.

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Enhancing time series aggregation for power system optimization models: Incorporating network and ramping constraints

In this paper, we extend a recently developed Basis-Oriented time series aggregation approach for aggregating input-data in power system optimization models which has proven to be exact in simple economic dispatch problems. We extend this methodology to include network and ramping constraints, for the latter, to handle temporal linking, we developed a heuristic that, in its current version, relies on the dual solution to find a partition of the input data, which is then aggregated. Our numerical results, for a 3-bus system, show that with network constraints only, we reduced the number of hours needed for an exact approximation by a factor of 1747, and a factor of 12 with network and ramping constraints. Moreover, our findings suggest that in the presence of temporal linking, aggregations of variable length must be employed to obtain an exact result (i.e., the same objective function value in the aggregated model) while maintaining the computational tractability. Our findings also imply that better performing aggregations do not necessarily correspond to commonly used lengths like days or weeks; additionally, we also prove that this input-data partition, based on the dual information, is always possible for these models independent of their size.

Code and Data Repository for Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

One of the most complicating factors in decentralized solution methods for a broad range of power system optimization problems is the modeling of power flow equations. Existing formulations for direct current (DC) power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. In this work, we present a novel sparsified variant of the injection shift factors formulation, which has a decomposable block-diagonal structure and scales well for large systems. We also propose a decentralized solution method, based on alternating direction multiplier method (ADMM), that efficiently handle transmission line outages in N-1 security requirements. Benchmarks on Multi-Zonal Security-Constrained Unit Commitment problems show that the proposed formulation and algorithm can reliably and efficiently solve interconnection-level test systems, with up to 6,515 buses, with no convergence or numerical issues.

Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

One of the most complicating factors in decentralized solution methods for a broad range of power system optimization problems is the modeling of power flow equations. Existing formulations for direct current power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. In this work, we present a novel sparsified variant of the injection shift factors formulation, which has a decomposable block-diagonal structure and scales well for large systems. We also propose a decentralized solution method, based on the alternating direction multiplier method, that efficiently handles transmission line outages in N-1 security requirements. Benchmarks on multizonal security-constrained unit commitment problems show that the proposed formulation and algorithm can reliably and efficiently solve interconnection-level test systems with up to 6,515 buses with no convergence or numerical issues. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was partially supported by Laboratory Directed Research and Development funding from Argonne National Laboratory provided by the Director, Office of Science, of the U.S. Department of Energy [Grant DE-AC02-06CH11357]. This work was also partially supported by the U.S. Department of Energy Advanced Grid Modeling Program [Grant DE-OE0000875]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0326 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0326 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Optimization of thermodynamic performance and mass evaluation for MW-class space nuclear reactor coupled with noble gas binary mixtures Brayton cycle

Space exploration technology is an important indicator of society's technological level. A space reactor coupled with a Brayton cycle is preferable for megawatt-scale space power systems. Noble gas binary mixtures have high chemical stability, heat transfer performance, and compressibility, making them the principal choice of working fluid for the space reactor Brayton cycle, which is also the key factor affecting the thermodynamic performance and mass of the system. This study developed thermodynamic performance and mass evaluation models for the space nuclear Brayton cycle and discovered the inherent relationship between system thermodynamic performance and mass. The effects of noble gas binary mixtures on system performance and mass were investigated. The results indicated that the elevated molar mass of noble gas binary mixtures reduced the aerodynamic load and mass of the turbomachines and increased the mass of the recuperator. There are optimal values of the total mass, specific mass of the system, and working fluid composition. Helium-xenon mixture is the optimal working fluid because it can achieve the highest thermodynamic efficiency and lowest mass. Furthermore, the optimal scheme of the helium-xenon Brayton cycle for a space nuclear power system was obtained by multi-objective optimization. Its power generation efficiency, specific mass, and helium molar fraction in the helium-xenon working fluid are 29.04%, 5.65 t∙MW−1 and 77.5%, respectively. This study provides a reference for the design and optimization of space nuclear power systems.

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Purpose: This study thoroughly investigates the impacts of Electric Vehicles (EVs) and Renewable Energy Sources (RES) on line losses within the IEEE 14-Bus System, with a particular emphasis on addressing challenges and optimizing power system planning. The primary purpose of this study is to assess and understand the intricate relationships between EVs, RES, and line losses within the IEEE 14-Bus System. The research aims to provide insights that contribute to the development of tailored strategies in power system planning, addressing challenges posed by EV adoption and RES integration. By exploring the impact on grid efficiency, sustainability, and reliability, the study aims to guide future power system planners in optimizing the integration of EVs and RES.
 Materials and Methods: The study utilizes load flow analysis, specifically the Newton-Raphson method, to model and simulate scenarios reflecting EV charging and discharging dynamics alongside intermittent RES integration within the IEEE 14-Bus System. A 24-hour dynamic load flow analysis is conducted to capture the diverse and dynamic impacts under varying load conditions. This comprehensive approach allows for a detailed assessment of line losses in the presence of EVs and RES.
 Findings: The study reveals nuanced impacts, indicating higher EV adoption and increased RES integration result in notable escalations in line losses. This highlights challenges associated with grid efficiency, emphasizing the need for tailored strategies in power system planning.
 Implications to Theory, Practice and Policy: The study advances theoretical understanding of dynamic interactions between EVs, RES, and power systems. In practice, it underscores the importance of adaptive control mechanisms and optimized strategies for accommodating EVs and RES while minimizing line losses. On a policy level, the findings suggest the need for regulatory frameworks incentivizing sustainable energy practices and technological research to mitigate challenges posed by EV adoption and RES integration.

Impact of supporting power source on a high-proportion renewable energy generation base

In recent years, China has organized three batches of high-proportion renewable energy generation bases, with the aim of resolving the geographical imbalance between renewable energy supply and consumption. Renewable electricity is transported from the resource-abundant western regions to the electricity-demanding eastern regions. Given the inherent variability and unpredictability of wind power and photovoltaic power generation, there is a pressing need for additional support from more reliable energy generation sources, including coal-fired power and concentrated solar power (CSP). This paper presents a power system optimization planning model that incorporates the internal energy transfer of CSP. It proposes strategies for establishing high-proportion renewable energy generation bases supported by both coal-fired power and CSP. Furthermore, it investigates the primary constraints affecting the energy generation base. The study also compares the peak shaving depth and output characteristics between coal-fired power and CSP. Moreover, this paper introduces the method of natural gas supplemental combustion to enhance the power supply stability of CSP, enabling the renewable energy generation base to supply electricity through conventional DC transmission.

Optimal Reactive Power Dispatch problem integrating probabilistic model of wind power for uncertainties in load using JAYA algorithm

The optimal reactive power dispatch (ORPD) is a method for optimising power system engineering to reduce power loss and voltage deviation. This work addresses the ORPD problem considering load uncertainty, where load is varied in ten different instances. The optimal conditions have been determined for the system with minimum power loss and minimum total voltage deviations for different loading instances. The problem has also been integrated with the probabilistic nature of wind power, calculated using the Weibull Probability Distribution Function (WPDF). The aim is to obtain the optimal values of wind power using the WPDF for two separate cases: overestimation and underestimation, integrate these power individually, and minimise both the power loss and voltage deviation for the ten different loading scenarios. This hybrid wind power integrated ORPD problem, considering load uncertainty, has been tested on the IEEE 30 and IEEE 57 bus systems using the JAYA algorithm and other optimisation techniques. This tuning-parameter-free JAYA algorithm has obtained the best solutions for power loss of 1.8641 MW and TVD of 0.123 p.u. for the overestimated wind power integration at the IEEE 30 bus system at 100% loading compared to the other algorithms. It also acquired the best power loss and TVD solutions of 1.9144 MW and 0.1245 p.u., respectively, for the underestimated wind power at the same bus system for 100% loading with the highest convergence rate. The overall study establishes the JAYA algorithm as a superior optimising technique for solving this hybrid ORPD problem compared to the other techniques.

A Review and Comparison of FACTS Optimal Placement for Solving Transmission System Issues

The current social impact of new transmission and distribution lines and the growth in environmental requirements has not lead to the expansion of the electric power grid but to the optimization of the existing assets. Flexible AC Transmissions Systems (FACTS), developed during the last decades of the past century, have become one of the most remarkable solutions for the optimization of the electrical power grid. Due to their flexibility, different types of FACTS have been proposed to solve similar transmission system operation problems. This paper reviews the FACTS devices used for solving these problems and the techniques used to optimize their location. The objective of the paper is to serve as a guide for selecting the right power system analysis and optimization technique for a given transmission system problem, and the most used FACTS for this purpose. The main operation issues that have been considered are: voltage control, assets optimization, line overloads and grid congestion, voltage stability problems, angle stability problems, contingencies and economic issues. In this study, the power system analysis and optimization methods have been divided into four main groups: classic optimization methods, technical criteria based methods, heuristic or meta-heuristic based methods and simulation based methods.