Planning Of Electric Power Systems Research Articles

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

Fluent Restoration Modelling Applied to a Real Power System Using Colored Petri Nets

In this article the Colored Petri Nets (CPN) and the Critical Path Method along with the Program Evaluation Review Technique (CPM/PERT) are described and their characteristics compared for application on the real-time fluent restoration of power systems. A CPN based method is proposed and has been found more suitable since it is able to represent the temporal sequencing and the logical dependency between restoration activities. The restoration planning of electrical power systems are based on a worksheet drawn up by experts, which contains a list of procedures for restoration. The restoration process has been improved and a real case study is presented using the CPN modeling applied with success to an operative fluent restoration post-outage in the Brazilian interconnected system

Development of an interval double-stochastic carbon-neutral electric power system planning model: A case study of Fujian province, China

Synergic adoption of diverse measures and consideration of multiple uncertainties are essential for electric power systems to achieve carbon neutrality. To fill the research gap, an interval double-stochastic carbon-neutral electric power system planning (abbreviated as IDS-CEP) model is developed. In the IDS-CEP model, reduction measures include electricity mix improvement, CO2 capture, transport, and storage (including monoethanolamine absorption and membrane separation), CO2 direct air capture (containing solid adsorption technique and liquid adsorption technique), ecosystem carbon sinks (forest, farmland, and ocean); uncertainties involve stochastic electricity demand, CO2 emission allowance, CO2 emission coefficient, and other interval parameters, which are quantified as forty-five scenarios and three levels. Through the application in Fujian province (China), results disclose that: (i) combination of the reduction measures would help Fujian achieve carbon neutrality with minimized system cost by 2060, their contributions could averagely be ranked as electricity mix improvement (70.75%) > forest (6.03%) > monoethanolamine absorption (5.98%) > farmland (4.53%) > solid adsorption technique (3.83%) > membrane separation (3.45%) > ocean (3.35%) > liquid adsorption technique (2.08%); (ii) to guarantee electricity supply, total installed capacity should be expanded to [156.16, 197.78]GW, where low-carbon electricity (especially nuclear, wind and photovoltaic electricity) installed capacity would occupy [74.68, 76.66]% by conducting electricity mix improvement to reduce CO2 emission; (iii) the changes in electricity mix structure would lead to decreased water utilization (by [48.57, 61.54]%) and pollutant emissions (by [77.00, 87.35]%); (iv) uncertainties would significantly affect decisions about electricity generation and CO2-emission reduction, resulting in varied system costs (in a wide interval of [6.07, 9.16] × 1012 RMB¥). Authorities can choose appropriate schemes according to their risk attitudes towards uncertainties. Results provide scientific planning support for Fujian's electricity generation sector; meanwhile, the IDS-CEP model can also be used in other regions by updating data information.

Optimal Reactive Power Dispatch in Electric Transmission Systems Using the Multi-Agent Model with Volt-VAR Control

The optimal dispatch of reactive power is a fundamental task in the operational planning of electrical power systems. This task aims to minimize active power losses and improve voltage levels within the electrical power system. This paper presents the application of the particle swarm optimization methodology to achieve optimal reactive power dispatch. The methodology’s performance is demonstrated by its high processing speed and the results obtained through a comprehensive global search for reactive power dispatch. Additionally, experimental results confirm the algorithm’s effectiveness in optimizing the objective function across different case studies, highlighting its ability to achieve optimal reactive power dispatch. This study represents a significant advancement in the field of power system optimization and provides a useful tool for managing and controlling these systems.

Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System

The planning of electrical power systems in remote island areas poses a few challenges, such as requiring many load centers, various energy sources, and certain geographical conditions, which leads to inefficiencies in energy production. For this reason, it is necessary to plan an electrical transmission system to efficiently transfer the power between load centers. Previous research has found that the completion of the most economical power system will be achieved in 2050 on the condition that the Ambon, Seram, Haruku, and Saparua (Ambon-SHS) systems are interconnected in 2025. Providing complementary support, this paper conducts a power system analysis to assess the proposed interconnection system in Maluku Island, which has several islands, small load centers, and local renewable energy resources. The power system analysis was performed using DIgSILENT PowerFactory and was carried out every 5 years of the system planning period until 2050. The results show that the design of the obtained transmission system meets the standard security criteria, which include thermal loading and voltage, being under both normal and N-1 contingency conditions, a short-circuit current, voltage stability, frequency stability, and transient stability. Finally, this paper demonstrates that the proposed plan is economically and technically feasible.

Predicting Thailand Electricity Load Demand with Modified Fourier Series and Seasonal-Trend Decomposition Methods Using LOESS Transformation

Accurate long-term and midterm electricity load forecasting play an essential role in electric power system planning. Drawing on the seasonal-trend forecasting capacity of Fourier series and LOESS transformation, this paper applies modified Fourier series transformation (MFST) and modified seasonal-trend decomposition using LOESS transformation (MSTLT) to electricity load forecasting and compares the performance of two alternative models: the ARIMA(p,d,q) SARIMA(P,D,Q) model and the support vector regression (SVR) model. The data comprise monthly electricity consumption volumes between 2002 and 2019. The data between 2002 and 2018 are utilized to construct the forecasting model, while those in 2019 are employed to test the accuracy of the predicted values. The results confirm the validity of the proposed model in terms of forecasting accuracy and interpretability.

A SUPPLY AND DEMAND ANALYSIS FOR THE TURKISH ELECTRICITY MARKET: SUPPLY ADEQUACY AND RESOURCE UTILIZATION

Electricity is a critical commodity for which the demand should be met using reliable, cost-effective, and sustainable resources. The characteristics of supply and demand should be comparatively analyzed for efficient electric power system planning. This study examines Turkey's current electricity supply and demand and their characteristics for future balance. Turkey's installed capacity and electricity consumption per capita are analyzed using historical data, and trends for future electricity demand are estimated. Electricity supply based on generation sources is reviewed, and the historical contribution of each source to total generation is shown. Statistical distribution of electricity generation by generation types and their relationships are also investigated. As the supply adequacy should be carefully reviewed for sustainability, finally, the characteristics of supply are analyzed using capacity factors, reserve margins, capacity margins, load duration curves, and peak-to-average demand ratios. It is shown that although the margins are above comfortable levels after 2017, they still need to be increased as the increased use of renewables might require extra availability. The load duration curves and peak-to-average demand ratios have shown that the density of demand is increasing, and capacity needs to be expanded and utilized carefully in the future for the reliability of the power system.Keywords: Electricity Supply-Demand, Electricity Generation Sources, Consumption Trends, Capacity Factor, Reserve Margin, Load Duration CurveJEL Classifications: Q41, O13, L94DOI: https://doi.org/10.32479/ijeep.11412

Grid Resilience analysis and planning of electric power systems: The case of the 2021 Texas electricity crises caused by winter storm Uri (#TexasFreeze)

The frequency of Very Large-Scale Weather Events (VLSWEs) such as Superstorm Sandy in 2012, Hurricane Maria in 2017 and Winter Storm Uri in 2021, among others, in the last decade have led increased conversations around the topic of grid resilience, by grid operators, planners, government agencies and other industry stakeholders. These events, which were to only occur “once in 100 years” exert a heavy toll on life and property. For example, winter storm Uri and the statewide power outages it caused in Texas directly and indirectly led to the death of 210 people and by some estimates caused over $120 billion in economic losses. The 2021 Texas electricity crises, which led to over 4 million people losing electricity and water for almost a week has brought about a renewed interest in the resilience of the electric power system in general and the Electric Reliability Council of Texas (ERCOT) in particular. Historically these conversations have been about grid reliability, but with the increase in frequency and the sheer size of devastation caused by VLSWEs more holistic and robust approaches to addressing their effects on the grid are being considered by industry stakeholders and policy makers. In this paper the events of this crisis are analyzed using a syncretistic approach developed from analyzing and then synthesizing important resilience constructs, frameworks and concepts.

Standardizing Demand-Side Management: OpenADR and its impact

The terms generation, transmission, and distribution are the categories under which operations and planning of electric power systems have traditionally been discussed. Notable by their absence are the facilities in which the electricity that is generated, transmitted, and distributed is consumed-homes, buildings, and industrial plants. This exclusion was logical when generation was centralized and dispatchable; the supplydemand balance could be maintained through actions at generators. Variations in consumption, whether predictable in the short or long term or the result of unforeseen contingencies, could be addressed by increasing or decreasing kilowatts produced.

Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic streamflow and wind speed time series: A Brazilian case study

Wind variability and hydro-wind complementarity should be both considered in the planning of electrical power systems. In such cases, the spatio-temporal dependence between streamflow and wind speed regimes needs to be asses to balance the electricity generation. Medium-term hydrothermal scheduling problems (MTHS) are used to define operation policies for electrical power systems under 5–10 years horizon. MTHS uses stochastic optimization techniques fed by synthetic streamflow scenarios. To set better operation policies, such scenarios should well represent statistical features of historical data. With the rapid growth in the installed capacity of wind power, operators are encouraged to consider novel approaches to represent the dependence of hydrometeorological variables. This study integrates wind variability and hydro-wind complementarity in the medium-term planning of electrical power systems employing joint simulation of periodic streamflow and wind speed time series. The generated scenarios are used as input to derive monthly operational policies via Implicit Stochastic Optimization. A hydropower plant and a wind farm, both located in the Northeast region of Brazil were selected as a case study. Results show that considering wind variability and hydro-wind complementarity can significantly reduce energy deficits in power systems and increase the flexibility in the operation of water reservoirs.

A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

Market-Based Integrated Generation Expansion Planning of Electric Power System and District Heating Systems

As the primary heat source in the district heating systems (DHSs), combined heat and power (CHP) units are playing an increasingly significant role in electricity generation. Considering that, this paper innovatively proposes an integral generation expansion planning (GEP) framework, which enables competition to occur among conventional power plants (CPPs) of the GENCOs and CHP units of the DHSCOs. The proposed framework is based on decentralized decision-making which involves an iterative process for simulating the interactions of ISO, GENCOs, and DHSCOs. The combined heat and power dispatch model is reconstructed to determine the LMPs to offer incentives for market participants' investments. The fuzzy load duration curve (FLDC) method is proposed to model the electricity load that would influence the LMPs. In each FLDC state, uncertain load is represented by a fuzzy number. Investment decision-making models of the GENCOs and DHSCOs are formulated as a mixed integer linear programming with fuzzy objectives (MILPFO). A technique is proposed to solve the MILPOF models with the consideration of the risk appetite of the market participants. The examples illustrate the benefits of integrated planning of electric power system and DHSs.

A Stochastic Optimization Model for Carbon-Emission Reduction Investment and Sustainable Energy Planning under Cost-Risk Control

Restricted by conventional energy resources and environmental space, the sustainable development of urban power sector faces enormous challenges. Renewable energy generation and carbon capture and storage (CCS) are attractive technologies for reducing conventional energy resource consumption and improving CO2 emission mitigation. Considering the limitation of expensive investment cost on their wide application, a stochastic optimization model for the optimal design and operation strategy of regional electric power system is proposed to achieve conventional resource-consumption reduction and CO2 emission mitigation under cost-risk control. The hybrid method integrates interval two-stage stochastic programming with downside risk theory. It can not only effectively deal with the complex uncertainties expressed as discrete intervals and probability distribution, but also help decision-makers make cost-risk tradeoff under predetermined budget. The proposed model is applied in the electric power system planning of Zhejiang Province, an economically developed area with limited fossil energy resources. The influences of different resource and environmental policies on the investment portfolio and power system operation are analyzed and discussed under various scenarios. The results indicated that different policies would lead to different generation technology portfolios. The aggressive CO2 emission reduction policy could stimulate the development of CCS technology, and the electric power system would still heavily rely on coal resource, while the tough coal-consumption control policy could directly promote regional renewable energy development and electric power structure adjustment.

Operation and Planning of Electric Power Systems Utilizing Big Data in Electric Energy Field

Operation and Planning of Electric Power Systems Utilizing Big Data in Electric Energy Field

Load management possibilities of electric vehicles when expansion planning of electric power systems

Electrical load management is undoubtedly an integral and paramount component of power industry functioning. Electric and hydrogen vehicles are not an exotic vehicle today and the most perspective for solving this problem. The paper studies the impact of advanced environmentally compatible vehicles on electric power system expansion. The potential of their use as load-controlled consumers is assessed. The results of study in the area of electric power system expansion optimization considering this potential for electricity consumption control are presented.

Multi-criteria decision analysis for renewable energy integration: A southern India focus

In recent days, sustainability is considered as an important mechanism due to contemporary increase in demands and worldwide limited resources. This paper presents the possibility of integrating a renewable energy system with an existing grid to meet electrical energy demand of institutional buildings located in Indian state of Tamil Nadu. Currently, the Tamil Nadu state electric-grid power is not surplus and experiencing 40% short fall in generation. In this present paper a modern approach for the optimum planning of electric power system (EPS) is proposed based on the Analytic Hierarchy Process (AHP). An intertwined analysis on energy management and techno-economic optimization of grid connected renewable energy system is proposed. The prospects of different fixed tilt solar panels and peak load shifting based energy management are performed through HOMER Energy® simulation. The AHP multi-criteria decision analysis reveals that annual optimum tilt grid connected photovoltaic system is the optimum configuration for study location. The effectiveness of the AHP approach is evaluated with best-worst method and stochastic multi-criteria acceptability analysis for prioritizing the renewable energy system options in order to select best EPS. In addition, the optimum configuration is implemented in the institutional buildings and performance is analyzed under varying climatic conditions.

Multi-stage generation and transmission coordinated planning method with a modified fireworks algorithm facing to energy internet

The integration of energy internet (EI) and electric power system has a profound influence on electric power system planning. Therefore, this paper mainly resolves the EI-oriented multi-stage generation and transmission coordinated planning problem with a modified fireworks algorithm. Firstly, we combined with Stackelberg game theory and focused on subject game theory, system low carbon characteristic and renewable energy generation, a multi-stage generation and transmission coordinated planning model for electric power system is proposed. The model has a main objective function of maximizing the social welfare and two secondary objective functions of maximizing individual profits in the operation stage and maximizing overall profits of generation companies in the planning stage. Then, a modified fireworks algorithm (MFWA) is proposed with good expandability, convergence and adaptability to solve the problem. The MFWA is modified by introducing Gaussian Mutation to improve global search capability. The basic coding method and solution procedure of the MFWA are depicted. Finally, an improved IEEE-24 bus system is used to test the proposed planning method and MFWA. By comparing three scenarios and analysing results, we can verify the validity and feasibility of the proposed method.

Investigation of static transmission expansion planning using the symbiotic organisms search algorithm

ABSTRACTTransmission expansion planning (TEP) has become a complex problem in restructured electricity markets. This article presents the symbiotic organisms search (SOS) algorithm, a novel metaheuristic optimization technique for solving TEP problems in power systems. The SOS algorithm is inspired by the interactions among organisms in an ecosystem. The TEP problem is formulated here as an optimization problem to determine the cost-effective expansion planning of electrical power systems. Several constraints, such as power flow of the lines, right-of-way validity and maximum line addition, are taken into consideration. First, the SOS algorithm is tested with several benchmark functions. Then, it is applied on three standard power system networks (IEEE 24-bus system, Brazilian 46-bus system and Brazilian 87-bus system) in a TEP study to demonstrate the optimization capability of the proposed SOS algorithm. The results are compared with those produced by other state-of-the-art algorithms.

HVDC Grid Segmentation Analysis for Blackouts Reduction

Segmentation of large AC systems through DC links introduces a new concept that utilizes the advantages of direct current transmission to improve network reliability and increase power transfer capacity. Technical literature argues that the segmentation of the AC network and the introduction of DC links at these systems connection points bring benefits to system operation, once contingencies generated on one side of the DC connection point would not be reflected on the other side of DC connection, thereby reducing the likelihood of cascading shutdowns and blackouts due to load restraint on transmission lines and transformers. Amidst this scenario, this paper presents a study of the main topics regarding the use of this new network segmentation philosophy, bringing a practical point of view for the use of this concept at the electrical power system planning. The effect of DC segmentation before a contingency that would initiate major outages in an adapted electrical system model IEEE-14 bus is studied and simulations have been performed with test HVAC systems and segmented by HVDC link. The results have been compared, principally in relation to the voltage bus, reactive power generation, system losses and power flow at the lines, and demonstrated that this new concept improved the grid reliability.