Effective Load Carrying Capability Research Articles

Enhancing the resilience of zero-carbon energy communities: Leveraging network reconfiguration and effective load carrying capability quantification

The integration of distributed energy resources (DERs) into contemporary energy communities (ECs) has revolutionized power systems, fostering sustainable and clean energy infrastructures. This paper focuses on the effective load-carrying capability (ELCC) to enhance grid resilience in the presence of DERs. We introduce an innovative network topology-based optimization framework that seamlessly integrates economic and resilience metrics within ECs while reducing carbon emissions. The Pareto front of non-dominated solutions for the proposed three-objective optimization problems is extracted, providing a comprehensive visualization of the trade-off between economic, resilience, and emission objectives, enabling informed decision-making. Analytical results, validated on the IEEE 33-bus test system, demonstrate the effectiveness of DER-based ELCC quantification in managing load supply during emergencies. Case studies show how the synergy between economic and resilience-based metrics significantly enhances grid resilience. The proposed framework has diverse applications, including enhancing grid adaptability to climate change, promoting sustainable energy integration, optimizing demand response strategies, and supporting the transition to a decarbonized energy community. This work addresses the challenges and opportunities in the evolving energy landscape, emphasizing the importance of our approach in achieving a cleaner and more resilient energy future.

Marginal vs. average effective load carrying capability: How should capacity markets deal with alternative generation forms?

Measuring the contribution of variable and supply-limited generation resources to grid reliability is becoming increasingly relevant as such resources expand their role in the electricity grid. Effective Load Carrying Capability (ELCC) has arisen as an approach to address these challenges. ELCC measures a resource's contribution to reliability based on the load that can be satisfied by adding the resource to the grid. ELCC advances resource accreditation by better reflecting the realities of how supply alternatives contribute to system reliability. It is not, however, a panacea. Implementing an ELCC approach requires challenging judgment calls that, because of the financial consequences for supply resources, are sure to generate controversy. The two primary methods for implementing ELCC, marginal and average, cause market inefficiencies. We compare the two methods using an economic model. The results of our model suggest that, although both methods result in inefficiencies under assumptions consistent with existing market conditions, the inefficiencies under a marginal approach are smaller than the inefficiencies under an average approach. Fortunately, there appear to be ways of addressing the adverse consequences of each method.

Key Factors Affecting the Capacity Value of New Energy and Sensitivity Analysis

The capacity credit (CC) of new energy describes the contribution of new energy to capacity adequacy and power system reliability. Many factors affect the CC of renewable energy systems. It is necessary to study various measures to improve the capacity credit of new energy. In this paper, the CC assessment model of a (photovoltaic) PV power plant based on the effective load carrying capability (ELCC) method is established by the universal generating function method. In addition, the sensitivity analysis of eight different key factors affecting the CC of photovoltaic power plants, such as PV penetration, number of PV output states, PV module failure characteristics, and other eight key factors, is carried out to point out how various factors affect the CC of the new energy. Analyzing the degree and depth of influence of key factors on new energy CC, which can provide reference for new energy CC.

Investigating capacity credit sensitivity to reliability metrics and computational methodologies

Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning their integration with the grid. The difficulties stem from the natural characteristics of variability and intermittency of wind and solar sources. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. This paper presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used in this paper. Then, this work compares the two techniques and shows the superior performance of the optimization approach. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems consist of photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this work, three different scenarios are used to compare capacity credit: system as a whole, only wind, and no batteries. Finally, sensitivity analysis is carried out to examine the impact of varying the wind speed, solar irradiation, and load. It is demonstrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability.

An efficient method to estimate renewable energy capacity credit at increasing regional grid penetration levels

The wide scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonize the electric grid. One challenge to reliably operating the grid is ensuring that sufficient generating capacity is available to meet demand at all hours. By determining an individual generator's contribution to resource adequacy based on its expected availability when power is needed, the capacity credit for these resources is estimated. The objective of this study is to quantify the contribution of VRE to resource adequacy as a function of VRE penetration, across several regions, technologies, and resources. A computational model was built using the effective load carrying capability (ELCC) method to calculate capacity credit values for regions spanning the contiguous United States. As the deployment of VRE increases, we show its marginal contribution to meeting peak load decreases, which in turn requires additional generating capacity to maintain reliability. In addition, a rapid approximation method is demonstrated to estimate solar and wind capacity credit, relying on the capacity factors during hours of peak net demand. We find that estimates with the lowest error relative to capacity credits calculated using the ELCC method occur using the average renewable resource capacity factors of the top net 10 demand hours, regardless of resource type. Using context-specific values for capacity credit can improve long-term decision making in generation capacity expansion, cultivating more economical long-term resource planning for deep decarbonization.

Reliability benefits of wide-area renewable energy planning across the Western United States

In response to near- and long-term market and policy shifts, power systems are integrating increasingly-distant renewable energy resources. No analyses quantify the spatial heterogeneity of resource adequacy contributions of renewables at the interconnection-scale in the United States, which would indicate the value to resource adequacy of integrating increasingly-distant renewables. To fill this gap, we develop a Monte Carlo-based probabilistic reliability model to estimate the effective load carrying capability (ELCC) of wind or solar generators. We apply our model to estimate the ELCCs of new solar or wind generators at 1406 locations across the Western Interconnection when contributing to each of five power pools within the Interconnection. Across the five power pools, we find solar ELCCs range from 0% to 44% and wind ELCCs range from 0% to 55% across the Western Interconnection. Solar ELCCs increase from east to west for all but one power pool, incentivizing integration of western renewables into eastern power pools. Deploying storage with wind or solar generators increases their ELCCs, with larger increases occurring at locations with lower renewable-only ELCC values. Thus, resource adequacy gains from deploying storage with renewables can partly substitute for those from integrating increasingly-distant renewables.

Generation Reliability Enhancement based on Reliability Indices

Reliability of generation system is an important aspect of planning, designing and operating for forecasting the plant capacity growth to make assurance that the totality generation is adequate to secure demand. The probability distributions correlating with the reliability indices may be utilized as complementary measures for accurate description and worthy information for system planners and operators. In the current research, the yearly load demand and its impact on the reliability of generating units was highlighted by some reliability indices that standardize against the international usefulness custom which are, Loss of Load Probability LOLP, Loss of Load Expectation LOLE, Loss of Energy Expectation LOEE, Loss of Energy Probability LOEP and Expected Energy Not Served EENS. This article is also dealing with the issue of generation capacity reserve assessment and the relation between the system reliability level and the expansion of the load. The notion of capacity extending analysis is clarify using a system of 4 stations. The effect on system reliability of adding a generation unit to the overall system can be observe in terms of decreasing the reliability indices and increasing the system Effective Load Carrying Capability (ELCC). This article has been used the Loss of Load Expectation LOLE in terms of day/ year as a critical limit for reliable and unreliable system.

Multi-objective optimal operation of pumped-hydro-solar hybrid system considering effective load carrying capability using improved NBI method

Hybrid renewable energy system (HRES) is an effective approach to aggregate multiple renewables efficiently. This paper focuses on the optimal operation of a hybrid system consisting of pumped hydro storage, cascade hydropower station, run-of-river hydropower station and photovoltaic plant. Various elements and contradictory aims like reliability and economy makes the operation of HRES complex. With this regard, a multi-objective optimization model is proposed to maximize both effective load carrying capability (ELCC) and economic revenue of the hybrid system. An improved normal boundary intersection (NBI) method is proposed to solve the multi-objective problem and obtain the Pareto surface. Moreover, to ensure the optimality of the results, big-M method, binary expansion method integrated with linearization transformation techniques are employed to linearize the model. Results are presented and discussed in the case study, which verify the feasibility of the model, demonstrate the effectiveness of the improved NBI method and illustrate the significance of involving ELCC into HRES operation.

탐라해상풍력발전단지의 확률적 시뮬레이션 방법을 이용한 Capacity Credit의 평가

This study is described an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG) with multi-state and aims to analysis the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability level. An analysis on generated capacity of wind power generator with Tamla offshore wind farm, which owned by KOEN(Korea South-East Power Co.Ltd.) and without Tamla offshore wind farm has been done in terms of Effective Load Carrying Capability(ELCC) and Capacity Credit(C.C.) in power system from the perspective of Loss of Load Expectation(LOLE). Case studies in this paper demonstrates how it changes the ELCC and C.C. according to the changing of wind speed in the Tamla offshore wind farm.

A Contribution Analysis of Tamla Offshore Wind Farm by Using of Probability Simulation Method

Abstract This study aims to analyze the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability levels. It is also described an algorithm for probabilistic production cost credit evaluation of Wind Turbine Generators (WTG). An analysis on generated capacity of wind power generator with Tamla offshore wind farm according to two different average wind speeds such as 11.8 [m/s] and 12.8 [m/s], and without Tamla offshore wind farm have been done in terms of Effective Load Carrying Capability (ELCC) and Capacity Credit (C.C.) in power system from the perspective of Loss of Load Expectation (LOLE). According to the changing of wind speed in the Tamla offshore wind farm, this paper demonstrates how it changes the ELCC and C.C. shown as case studies.

Power System Reliability Evaluation Including Capacity Credit Considering Wind Energy with Energy Storage Systems in China

Abstract This paper is based on power system reliability evaluation on a power system. This research focuse on finding the best case of using large scale wind turbine generator (WTG) with multi-energy storage systems (multi-ESSs). By the way, calculate the capacity credit (C.C.) and effective load carrying capability (ELCC). In the future, renewable energy will occupy a large part of the electricity market. However, the reliability of power system can be influenced by the large scale WTG installed in the system. Multi-ESSs can be installed with the WTG used to decrease the fluctuation of power system reliability caused by the uncontrollable wind resource. The purpose of this paper is analyze the power system reliability in power system by the index of ELCC and C.C. by using multi-ESSs coordinated to the power system with renewable energy generators (REG). Monte Carlo simulation (MCS) method is employed to evaluation the reliability of power system with WTG and multi-ESSs operating strategies.

Capacity value of energy storage in distribution networks

Abstract Security of supply in electricity distribution networks has been traditionally delivered by conventional assets such as transformers and circuits to supply energy to consumers. Although non-network solutions, such as energy storage (ES), can also be used to provide security of supply by carrying out peak shaving and maintaining supply for the duration of a network outage, present network design standards do not provide a framework for quantifying their security contribution and corresponding capacity value. Given the fundamentally different operating principles of ES, it is imperative to develop novel methodologies for assessing its contribution to security of supply and enable a level playing field to be established for future network planning. To this end, a novel probabilistic methodology based on chronological Monte Carlo simulations is developed for computing the Effective Load Carrying Capability (ELCC) of an energy storage plant. Substantial computational speed-up is achieved through event-based modelling and decomposing between energy and power constraints. The paper undertakes, for the first time, the in-depth analysis of key factors that can affect ES security contribution; plant and network outage frequency and duration, network redundancy level, demand shape, islanding operation capability and ES availability. ES capacity value is shown to decrease in networks with an unreliable connection to the grid; time to restore supply is shown to be more important that frequency of faults. Capacity value increases in cases of peaky demand profiles, while the ability to operate in islanded conditions is shown to be a critical factor. These findings highlight the need for sophisticated network design standards. The proposed methodology enables planners to consider ES solutions and allows network and non-network assets to compete on an equal basis for security provision.

Capacity expansion model of wind power generation based on ELCC

Capacity expansion is an indispensable prerequisite for power system planning and construction. A reasonable, efficient and accurate capacity expansion model (CEM) is crucial to power system planning. In most current CEMs, the capacity of wind power generation is considered as boundary conditions instead of decision variables, which may lead to curtailment or over construction of flexible resource, especially at a high renewable energy penetration scenario. This paper proposed a wind power generation capacity value(CV) calculation method based on effective load-carrying capability, and a CEM that co-optimizes wind power generation and conventional power sources. Wind power generation is considered as decision variable in this model, and the model can accurately reflect the uncertainty nature of wind power.

Net Load Carrying Capability of Generating Units in Power Systems

This paper proposes an index called net load carrying capability (NLCC) to evaluate the contribution of a generating unit to the flexibility of a power system. NLCC is defined as the amount by which the load can be increased when a generating unit is added to the system, while still maintaining the flexibility of the system. This index is based on the flexibility index termed ramping capability shortage expectation (RSE), which has been used to quantify the risk associated with system flexibility. This paper argues that NLCC is more effective than effective load carrying capability (ELCC) in quantifying the contribution of the generating unit to flexibility. This is explained using an illustrative example. A case study has been performed with a modified IEEE-RTS-96 to confirm the applicability of the NLCC index. The simulation results demonstrate the effect of operating conditions such as operating point and ramp rate on NLCC, and show which kind of unit is more helpful in terms of flexibility.

ELCC Approach for Calculating Capacity Credit of Wind Power in Korea

The share of wind power generation in domestic renewable energy is expected to increase significantly due to nationwide effort to reduce greenhouse gas emission and to enlarge the portion of renewable energy in electricity generation mix. In this study, the capacity credit, which is the most important factor for evaluating the effective capacity of domestic wind power, is calculated by using the ELCC (Effective Load Carrying Capability) methodology. The ELCC method is known to have the most sophisticated rationale among the various capacity credit estimation methods. We used the single-year data (2014) for the reference system, power demand and wind utilization. CC has been calculated to be about 0.3091 when the wind power penetration rate is less than 1%, and CC has a tendency of decreasing steadily with increasing penetration rate. According to the survey, there is no previous research on national level CC for domestic wind power generation.

Factors influencing calculation of capacity value of wind power: A case study of the Australian National Electricity Market (NEM)

Calculation of wind power capacity values for risk assessment of power system adequacy has attracted great attention in the literature. And the most popular approach has been the Effective Load Carrying Capability (ELCC) method which allows for the consideration of key factors such as wind capacity factor, forced outage rates (F.O.R) of conventional power stations, system reliability targets, and the correlation between wind availability and system load. However, comparatively little attention has been paid to analysing the effects of other factors such as the number of wind farms and wind installed capacity, the length of historic time series data on demand and wind resources. This paper provides an in-depth analysis of how these factors influence the calculation of capacity value for the Australian National Electricity Market (NEM) power system using metered half-hourly wind and load data for 2006 to 2013. The analysis incorporates the periods with extreme risk events. Our results show that capacity values depend greatly on the design of the simulation model used, and highlight the importance of capturing wind and load data points relating to extremely high demand periods. We compare our NEM-wide estimates to recent estimates for the State of South Australia.

Effect of Wind Speed and Load Correlation on ELCC of Wind Turbine Generator

Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load make conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveal that the wind speed correlation with the load has a significant effect on the system reliability indices, such as Expected Energy Not Served (EENS) and Loss Of Load Probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the Effective Load Carrying Capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.

Defining and Evaluating the Capacity Value of Distributed Generation

Installed capacities of distributed generation (DG) are projected to increase substantially in Great Britain and many other power systems. This paper will discuss the definition of capacity value of DG arising from its ability to support additional demand without the need for new network capacity, in analogy with the definition of effective load carrying capability (ELCC) at transmission level. This calculated ELCC depends on the precise detail of its definition; in particular in a demand group fed by a pair of circuits where the double outage state dominates the calculated reliability index, the ELCC will be very small unless the generator can run in islanded mode. Finally, requirements for use in practical planning studies and development of formal planning standards will be discussed.

Effective Load Carrying Capability Evaluation of Renewable Energy via Stochastic Long-Term Hourly Based SCUC

This paper evaluates the effective load carrying capability (ELCC) of renewable resources, including wind and solar, via the stochastic long-term hourly based security-constrained unit commitment (SCUC) model. Different from traditional approaches which approximate ELCC of renewable resources using system peak loads, nonsequential block load duration curves, or rolling-based sequential methods, the stochastic long-term hourly based SCUC could accurately examine the impacts of short-term variability and uncertainty of renewable resources as well as chronological operation details of generators on hourly supplydemand imbalance and power system reliability in a long-term horizon. Uncertainties of hourly wind, solar, and load in a 1-year horizon are simulated via the scenario tree using the Monte Carlo method, and Approximate Dynamic Programming is adopted for effectively solving the stochastic long-term hourly based SCUC model. Variability correlations between wind speed and solar radiation are considered within the scenario sampling procedure. Moreover, parallel computing is designed with the pipeline structure for accelerating the computational performance of Approximate Dynamic Programming. Numerical case studies on the modified IEEE 118-bus system illustrate the effectiveness of the proposed stochastic long-term hourly based SCUC model and the Approximate Dynamic Programming solution approach for evaluating ELCC of renewable resources. This would help independent system operators (ISO) designs effective long-term planning strategies for operating power systems efficiently and reliably.

A Noniterative Method to Estimate Load Carrying Capability of Generating Units in a Renewable Energy Rich Power Grid

It is important to estimate the contribution of renewable generation units in the evaluation of system generation adequacy for power generation planning taking into account the demand and renewable generation correlation and uncertainty. The effective load carrying capability (ELCC) is usually used for this purpose. In this paper, a noniterative analytical method is proposed for estimating the peak load carrying capability (PLCC) and ELCC of conventional and renewable generation units. The proposed method is verified using the IEEE RTS and an electricity network in New South Wales, Australia, and the results are compared with other estimation methods. The results show that the correlation between demand and renewable generation influences the ELCC of a renewable generation unit—the higher the correlation, the higher the ELCC, and vice versa. The main contribution of this paper is the development of an analytical noniterative and computationally efficient technique, which accounts for the correlation between demand and available renewable generation.