System Expansion Planning Research Articles

THE IMPACT OF ELECTRIC POWER OUTAGE ON THE PROFITABILITY OF SMALL BUSINESSES IN NIGERIA: A CASE STUDY OF GWAGWALADA TOWN

This study examined the impact of electric power outages on the profitability of small businesses in Nigeria, using 50 randomly selected small businesses in Gwagwalada town as a case study. A Logit regression model was adopted, incorporating a mix of qualitative and quantitative variables. The Maximum Likelihood (ML) estimation method was utilized in estimating the Logit model. Findings from the estimated Logit model showed that electric power outages had a negative impact on the profitability of small businesses in Gwagwalada town, while small businesses' expenses on alternative electricity power supplies had a positive impact on profitability. However, both electric power outages and small businesses’ expenses on alternative electricity power supplies in Gwagwalada town had an insignificant impact on the profitability of the small businesses. Based on the findings of the study, it was recommended, among other things, that the government should undertake a national demand study to determine the exact electricity requirements to meet demand in the short, medium, and long terms. Furthermore, an integrated least-cost system expansion plan should be developed for the efficient deployment of the Nigerian electricity industry in the medium and long terms. There is also a need for diversification of electricity generation to improve the security of supply, as well as the development of capacity to generate electricity closer to demand centers to reduce technical losses. Additionally, the government should explore and develop measures to ensure the long-term supply of gas for electricity generation.

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network. The objective function of the problem aims to minimize operational costs, energy losses, and anticipated energy not supplied, and the voltage security index. The constraints of this problem include AC optimal power flow model, security, and reliability limits. Scheme extracts a convex-linear model for the formulation using the conventional linearization technique. Pareto optimization driven by the aggregation of weighted functions results in a single-objective framework for the lower-level problem, and the Karush-Kuhn-Tucker technique presents a single-level model for the scheme. The approach utilizes stochastic optimization has been adopted to model load, wind farms, and network equipment availability uncertainties. Finally, the study cases yielded numerical findings that showcase the efficacy of the proposed scheme in achieving the desired economic, operational, security, and reliability status within the transmission network. This is particularly true in situations where there is appropriate generation and transmission expansion.

Flexibility provision in the Swiss integrated power, hydrogen, and methane infrastructure

The renewable energy transition hinges on balancing energy supply and demand across seasons. This paper investigates the potential flexibility of Switzerland’s integrated power, hydrogen, and methane infrastructure to balance temporal mismatches while complying with national energy policies for sustainability and security. It develops an optimization method for energy system expansion and operation planning, filling crucial research gaps by (1) explicitly modeling power and gas transmission networks to guide technology placement and pinpoint network expansions, and (2) incorporating flexibility in power demand via shedding and shifting and in hydrogen and methane demands via price elasticity. The findings suggest that a 6.7-fold capacity expansion of variable renewables (i.e., photovoltaic, wind, run-of-river) by 2050 offsets nuclear phase-out and demand growth. The winter power gap is filled by power imports, hydropower generation, and gas turbines fueled by cost-effective hydrogen or methane imports. However, fuel embargoes escalate winter hydrogen and methane prices, reducing demand by 3.8%–10.4% and increasing domestic fuel production from biomass and excess renewable power in summer. To bridge the seasonal hydrogen and methane supply–demand gaps, up to 1.9 terawatt-hours of gas cavern storage is deployed in geologically viable locations, while costly tank storage plays a minor role. Power-to-gas requirements and power trade restrictions necessitate further renewable expansion, including 8.0 to 9.5 gigawatts of wind installations.

Expansion planning of hybrid electrical and thermal systems using reconfiguration and adaptive bat algorithm

_ This study introduces a comprehensive model for the concurrent expansion planning of various energy systems and their associated equipment. The need to reduce network costs, emissions, losses, and feeder loading, as well as to enhance network reliability and voltage profile, mandates the utilization of proper multi-objective planning models that respect all network constraints. The introduced framework includes units for generating both electrical and thermal energies. The model leverages conventional expansion alternatives such as the installation of new lines, network reconfiguration, rewiring, and the addition of new thermal and electrical generating units to the network. Expansion planning involves determining the optimal time, location, and type of new installations to meet future energy demands while minimizing costs and emissions. Reconfiguration refers to altering the network topology to improve reliability and reduce losses. The proposed expansion planning is formulated as a discrete, nonlinear, and non-convex optimization problem, which is solved using the Self Adaptive Learning Bat Algorithm (SALBA). This algorithm improves convergence speed and increases the diversity of the search population, enhancing the likelihood of finding the global optimum. Numerical simulations of the proposed methodology on two modified standard IEEE test systems corroborate the efficacy and feasibility of the suggested approach. Key innovations include the comprehensive modeling for concurrent expansion planning, the use of an advanced optimization algorithm, and a focus on reducing costs, emissions, losses, and feeder loading while enhancing network reliability and voltage profile.

Capacity Expansion Planning of Hydrogen-Enabled Industrial Energy Systems for Carbon Dioxide Peaking

Capacity Expansion Planning of Hydrogen-Enabled Industrial Energy Systems for Carbon Dioxide Peaking

A quasi-static time-series approach to assess the impact of high distributed energy resources penetration on transmission system expansion planning

Due to advancements in solar cell fabrication and inverter-based technology, as well as decreasing acquisition costs, solar distributed generation systems have emerged as a promising renewable source. Furthermore, electrification of the transportation system is an alternative promoted by many governments to mitigate the dependency on fossil fuels and achieve the goals of decarbonizing the economy. However, interconnecting a large quantity of distributed energy resources (DERs) to the power system reveals technical problems affecting the entire system. Planning coordinators and grid operators need to understand the full-spectrum impact of DERs on the power system to ensure secure and reliable grid planning and operations. The operation of a Hydro-Québec transmission system planned for 2030 is simulated in quasi-static time series mode with a massive integration of 1 million of electric vehicles and 1000 – 3000 MW of distributed photovoltaics. This paper provided findings on assessment of high DER penetrations impacts on the transmission system in terms of voltage control, mitigation means used, MVAR availability and consumption margin, generating unit optimal operation, and switching actions of several equipment.

A novel squirrel-cat optimization based optimal expansion planning for distribution system

AimIn recent years, renewable distributed generation (DG) has grown to deliver sustainable electricity with minimal environmental impact. However, renewable DG poses new provocation in the distribution system expansion planning problem (DSEP). To address those problems, this paper suggests a new mathematical model for distribution system expansion plans using a novel hybrid optimization strategy. MethodThe optimal expansion plan for the distribution system is achieved using the hybrid optimization model, named Squirrel Search Insisted Cat Swarm Optimization (SSI-CS) algorithm. The proposed hybrid optimization algorithm is developed with the incorporation of the characteristics features of Squirrel search optimization (SSA) algorithm and Cat Swarm Optimization (CSO) Algorithm to optimize the solar capacity, wind capacity and biomass capacity. This combination aims to strike a balance between global and local optimization, ultimately leading to better cost-effective results. The Distribution systems variables like DG type, size/capacity, location, real power, reactive power, and the solar and wind capacity during load demand uncertainty act as the input to the proposed hybrid optimization algorithm. The main objective of attaining minimal cost for the expansion plan of the distribution system is checked, and the cycle is repeated until obtaining the optimal solution (minimum cost). ResultThe experimental analysis using an IEEE-33 bus system with 5 system states is executed in MATLAB/Simulink. The suggested SSI-CS model attained a minimal operational cost of 7433.4 which better than GWO, SSA and CSO. ConclusionHence, the proposed SSI-CS shows promise as an efficient and effective approach for distribution system expansion planning.

Green hydrogen exports in New Zealand and Chile can improve electricity supply security if configured as local energy insurance

Extreme weather events, for example, prolonged droughts, are increasingly stressing electricity systems and threatening the achievement of energy transition goals. At the same time, countries such as Chile and New Zealand are developing strategies to export renewable energy through green hydrogen. This paper economically evaluates the use of future green hydrogen exports as strategic storage under the logic of energy insurance, based on the assumption that Chile and New Zealand will adopt a hydrogen economy. Our first contribution is to compare the marginal costs of re-electrified green hydrogen, for different scenarios, to the value of lost load in New Zealand and Chile. We then determine the marginal cost of energy produced from green hydrogen based on projected production, transportation, and re-electrification costs for the year 2030. Finally, we estimate the eventual penalties that exporters would incur in the case of breaking their contracts and the overall resulting system costs of using hydrogen for system security. We found that re-electrifying hydrogen can be competitive with conventional technologies at 3 USD/kg. At 1.5 USD/kg, hydrogen is more competitive than fossil fuel-based technologies, for both New Zealand and Chile. Thus, in 2030, it could make economic sense for New Zealand and Chile to use hydrogen as a strategic reserve. The system cost of the proposed insurance scheme in 2030 ranges from 0.53 to 1.76 USD/MWh, which is small compared to total electricity costs. These values can be used for power system expansion and operation planning.

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

An AQ-SCS Optimization Assisted Optimal Expansion Planning with Fixed and Switchable Capacitors for Distribution System

Distribution System has given renewable-based DGs like Solar PV and Wind turbines a lot of attention because of growing worries about global warming and the depletion of fossil fuels. This work proposes a unique hybridized optimization technique for a distribution system expansion plan utilizing an innovative theoretical framework. Two stages are used to tackle the issue with the distribution system expansion plan: master optimization and sub-optimization for every state of the system. The developed Aquila based Sand Cat Swarm (Aq-SCS) optimization method considers the distribution system factors, like DG type, size/capacity, location, PQ power, fixed capacitor, switchable capacitor, and the SPV/wind capacity with load demand uncertainties. The sub-algorithm is employed in conjunction with the DG allocation strategy produced by the master algorithm to deduce the state-dependent operating tactics for each individual DG unit with respect to active and reactive power. The suggested Aq-SCS optimization approach is created by combining the properties of the SCSO with the AOA models. The main objective of achieving the minimal possible cost for the DSEP is verified, and the cycle continues unless the best outcome (least cost) is reached. The analysis of the suggested approach is carried out in 4 cases, such as (i) Without DG and capacitor (ii) With capacitor and no DG (iii) With DG and no capacitor (iv) With both capacitor and DG. The analysis is made in IEEE 33 bus system. The Aq-SCS approach outperforms the conservative approaches SSI-CS, WHO, AQO, and SCSO, according to the analysis conducted in MATLAB/Simulink utilizing an IEEE-33 bus system with five system states.

Towards CSP technology modeling in power system expansion planning

Power systems with a high level of renewable energy penetration face challenges due to the inherent properties of some renewable energy sources, such as their variability and uncertainty. These systems require reliable technologies capable of handling large ramps and flexibility needs. Concentrating Solar Power (CSP) has been identified as one solution, which has been researched and implemented in several countries. This paper presents a novel analysis and evaluation of the impacts of choosing an inadequate representation of CSP technology in power system planning tools. The current state of CSP in power system expansion planning in the literature is examined, providing a classification for the modeling of investment and operation of CSP plants. Based on a proposed analysis framework, different models and configurations for CSP plants are evaluated and applied to a 5-bus test system. One of the key findings is that the selected model highly impacts the computational effort (150% more processing time), the quality of the planning decision (14% more overall costs), and the total emissions (up to 52% more emissions). A storage-based model proves to be the most suitable option, considering the operation and investment costs, as well as computational burden. The selected model achieves a 1% error compared to the results obtained with a reference model, while also requiring 40% less computation effort. The proposed framework can be applied to other power system structures by choosing the best suited CSP modeling.

Extended EDAS Analysis for Multi-Criteria Decision-Making Based on Distributed Generation (DG) Technologies System

Recently, there has been a growing interest in distributed generation (DG) technologies, driven by various factors such as fuel price uncertainties, environmental constraints, and increasing power consumption along with transmission capacity shortages, in modern power systems. DG, which involves utilizing clean and renewable energy sources for power generation within the distribution system, has gained significant attention globally. Many developing countries, including Libya, are considering the adoption of DG technologies as part of their energy system expansion plans. Libya, located in North Africa and characterized by vast desert lands, has abundant solar radiation, making solar energy a promising and sustainable source of power. However, despite this energy potential, the southern part of Libya faces frequent power outages. In order to effectively maintain service quality, it is essential to conduct quantitative evaluation of wireless sensor networks. the evaluation of wireless sensor networks involves addressing the multiple attribute group decision-making (MAGDM) problem. To tackle the challenges of MAGDM, an extension of the classical EDAS (Evaluation based on Distance from Average Solution) method is proposed in this paper. The proposed method incorporates interval-valued intuitionistic fuzzy sets (IVIFSs), which provide a more flexible and comprehensive representation of uncertainty, to handle the complexities of MAGDM. The paper begins with a brief review of essential concepts related to IVIFSs. Then, the weights of attributes are determined using the CRITIC method. Subsequently, the IVIF-EDAS method is established by integrating the EDAS method with IVIFSs, and all the calculation procedures are described.

Merchant EV charging station expansion planning

The significant expansion of Electric Vehicles (EV) creates an uncertain load demand that may pose many challenges to conventional planning methods, especially at the distribution network level. The development of EV technology requires charging infrastructure, which must be considered in the Distribution System Expansion Planning (DSEP). Due to the presence of EV and the load they impose on the distribution system, the planning of EV charging stations is done simultaneously with the DSEP so that the system operator can properly consider the technical constraints of the network and operational constraints. This paper presents a bi-level model for EV charging station expansion planning. The main purpose of the first level problem is expansion planning of charging stations and distribution system. But the charging stations are mostly built by a merchant investor, and they do so when it is profitable and has a reasonable risk. Therefore, the merchant investor should be encouraged to build a charging station in the planned areas. For this reason, in the second level problem, the profitability of the charging station and its risk are examined. To solve this problem, genetic algorithm (GA) has been used. Finally, to test the proposed model, the simulation was performed on a modified 69-bus IEEE network.

Generation Expansion Planning for Solvent-Storaged Carbon Capture Power Plants Considering the Internalization of the Carbon Emission Effects

Carbon capture power plants (CCPPs) can effectively eliminate the carbon-locking effect of coal-fired power generation systems, which constitute one of the essential technical pathways to achieve low-carbon transformation of the power structure. Capable of decoupling CO2 absorption and separation in terms of time, CCPPs equipped with solvent storage have stronger operational flexibility while promoting the realization of carbon emission reduction targets. Therefore, this study focuses on the generation expansion planning of power systems containing solvent-storaged carbon capture power plants (SSCCPPs). First, a mathematical model is proposed for SSCCPPs considering new operation characteristics of power generation-carbon capture. Second, the per kilowatt-hour carbon emission coefficient (kWh-CEC) of various power plants is analyzed by life cycle assessment (LCA), and a bilateral stepped carbon trading model is established to quantify power plants' whole life cycle carbon emission effects. Third, a generation expansion planning model considering the internalization of carbon emission effects is developed to explore the benefits of planning SSCCPPs in wind-coal intensive power systems and multi-energy integrated systems. The simulation results on the modified IEEE RTS-96 system and IEEE HRP-38 system have validated the economic and low-carbon performance of planning SSCCPPs in the system.

Enhanced proportional integral controller for DSTATCOM with particle swarm optimization algorithm in power system

Power system expansion planning is supported by using distributed generation (DG) instead of installing new main power system generators. The portability and autonomous operation of the DG make it capable of integrating into the distribution system. Power electronics devices for home loads, mainly electric vehicles, mobile phones, and laptops, introduce harmonics and related power quality issues to the distribution system. This load creates power quality issues like an increase in total harmonic distortion (THD) and a reduction in power factor. This paper uses the DG to supply important power electronics devices for the mitigation of power quality problems in distributed systems with non-linear loads, such as electric vehicles, called the “distributed static compensator (DSTATCOM)”. Wind and photovoltaic (PV) generators supply power to a common DC source. To improve power quality D-STATCOM powered by PV and wind energy systems at the DC link is incorporated. The direct axis/quadrature axis method (DQ method) controls the real and reactive components of the power. Proportional integral (PI) and particle swarm optimization (PSO) based PI controller parameter estimation are developed, and results are compared for power quality performance. In terms of improving power quality, the PSO-based parameter estimate of the PI controller performs better than the traditional PI controller.<br /><div class="extension_reset_css AnnotateNet_widget AnnotateReviewNotesButton" style="display: none; z-index: 2147483644;" data-id="ExtensionWidget"><div class="extension_reset_css AnnotateNet_widget annotateclickable" style="z-index: 8; display: none;" title="Resize" data-id="Widget"> </div></div>

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Low-carbon coordinated expansion planning of carbon capture storage and energy storage systems with VCG-based demand response mechanism

Recently, the decarbonization of the power industry has gained increasing attention as global warming problem has become more acute. However, the existing system planning models targeting to tackle emission reduction rarely consider to improve the flexibility of power system. In this paper, a co-planning model considering the expansion of carbon capture and storage (CCS) units and energy storage systems (ESSs) is proposed, in which the carbon emission target is embedded in the objective function via setting different carbon emission tax. Here, the system flexibility is improved by Source-Load-Storage (SLS) response, which is achieved via CCS units, ESSs and flexible load. In addition, a subsidy method for cost recovery based on Vickrey-Clark-Groves (VCG) mechanism is proposed to stimulate market members participating in SLS response. Numerical results from a modified IEEE 118-bus power system illustrate the impact on system cost and emission results owing to specific carbon emission tax, investment budget and SLS response. Finally, we display the contribution rate and subsidy of different participants in a typical day.

Frequency‐Constrained Expansion Planning in Competitive Market considering Renewable Failures

In modern generation expansion planning of power systems, installing grid‐connected renewable energy systems is preferred than thermal units due to their low generation cost and environmental pollution. However, the expanded power system must have ability to resist against any outages influenced on the frequency response of the system. So, several frequency‐constrained expansion planning models are extracted to provide a reliable infrastructure to manage the frequency behavior. The main distinction of our model with others is considering the failure of grid‐connected renewables in the expansion planning models. Furthermore, due to the lack of information about the uncertainty of malfunctions, a distributionally robust optimization approach is applied to the problem under several ambiguity radiuses. The results of implementing the proposed method on the IEEE RTS96 case show that increasing the penetration of malfunctioned units can lead to more investment on the thermal units to prevent frequency violation under any outage in the system. With increase of the Kullback–Leibler divergence from zero (stochastic) to 3 (robust), the cost of the robust model is increased about 0.02%. The model is designed for the deregulated market to increase the competition of market through maximizing their benefit and line congestion management with local marginal pricing techniques.

Stochastic security‐constrained transmission and energy storage expansion planning considering high penetration of renewable energy in integrated gas‐electricity networks

AbstractThis paper presents a new formulation for solving the expansion planning of transmission lines and energy storage systems while considering the integration of electricity and gas networks. The proposed model is a bi‐level stochastic planning model. It involves transmission and battery expansion planning at one level, and gas network modeling at the other. The study addresses the impact of high penetration of renewable resources and security constraints on both the electricity and gas networks within the context of network expansion planning. The proposed model is a stochastic mixed‐integer linear programming model at both levels, and its challenging solution is achieved through reformulation and decomposition methods. Two experimental networks are analyzed: a 6‐node network and the IEEE RTS 24‐bus network for the electricity network, coupled with 5‐node and 10‐node gas network systems. The results demonstrate the efficiency of the proposed model. Simulation results indicate that the proposed model is highly effective in scenarios where power and gas network lines are disconnected, preventing load shedding even when the integrated network lines are disconnected.