Capacity Credit Research Articles

Renewable-battery hybrid power plants in congested electricity markets: Implications for plant configuration

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. This paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4 h is 29.4 % for solar and 26.8 % for wind, assuming low battery degradation costs and storage sized to 100 % of the plant's nameplate generation capacity. Increasing storage duration beyond 4 h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90 % capacity credit with 4 h of storage, while wind hybrids require 8 h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

Capacity contributions of Southern Oregon offshore wind to the Pacific Northwest and California

Variable renewable energy generation poses unique capacity challenges, which increasingly depend on weather events at varying timescales. Facilitated by transmission planning, geographic and technological diversity of the generation fleet may provide a mitigation to capacity shortfalls. In this work, offshore wind (OSW) energy is sited in the areas off the West Coast between Coos Bay, Oregon, and Crescent City, California. Three generation and transmission scenarios are modeled within the Western Interconnection: (i) 3.4 gigawatts (GW) of installed OSW capacity connected to Southern Oregon through a High Voltage Alternating Current (HVAC) Radial Topology in 2030; (ii) 12.9 GW of installed OSW capacity connected to Washington, Oregon, and California through a High Voltage Direct Current (HVDC) Radial Topology post-2030, and (iii) the same 12.9 GW connected to the same locations through a Multi-terminal DC (MTDC) Backbone Topology post-2030. Zonal dispatch simulations assuming coincident wind, solar, and hydropower production and loads over 18 meteorological years, accounting for temperature-dependent equipment derating and forced outages, serve as inputs to the Associated System Capacity Contribution (ASCC) methodology. The capacity credit is 33%, 25% and 34% for the 2030 HVAC Radial Topology, 2030+ HVDC Radial Topology, and 2030+ MTDC Backbone Topology, respectively. Transmission design is shown to mitigate the typical erosion of marginal capacity contribution as more OSW is developed, underscoring the opportunity for grid modernization while decarbonizing the generation mix.

Variable renewable energy modeling system to study challenges that impact electrical load at different penetration levels: A case study on Kuwait's load profile

The transitioning from conventional power systems to variable renewable energy (VRE) could impose significant challenges on transmission, short-term balancing requirements, and more cycling to ramp conventional plants. Kuwait's government has set a target of 15% of electricity generation from renewable resources by 2030 and more in the following years. This ambitious target requires close collaboration between researchers and decision-makers to overcome the upcoming challenges. The current study comes in the context of this objective to support decision-makers technically during planning and deploying VRE plats in various parts of the country. An in-house simulation modeling system for solar photovoltaic (PV) and wind power based on high-quality weather data has been developed. The system can model centralized or decentralized VRE scenarios. Several load characteristics have been used to examine the output of the scenario, such as the residual load, impact on the peak, and baseload due to changes in VRE penetration level. Other parameters, including capacity credit, full load hours, ramping power magnitude and rate, and overproduction of resources, are also outputs of the system. The output results of the modeling system have been validated against two years of operational solar PV and wind Shagaya power plants data. The results show combining solar PV with wind could dramatically increase the capacity credit, reduce overproduction, and reduce ramping power. The authors will continue evolve and improve this in-house modeling system to support research in this field and for the upcoming future RE projects instead of relying on commercial software.

Digital workflow for nearly zero-energy high-rise office building design optimization at the district scale in Mediterranean context

While the implementation of sustainable urban planning has proven to be one of the primary goals to reduce the climate change impact, the rapid adoption of nearly Zero Energy Buildings (nZEB) concept in the building sector is inevitable to reach that objective. Following this trend, this article focuses on the implementation of a parametric digital workflow to evaluate the energy performance of a nearly zero energy high-rise 23-storey office building in the climatic and urban contexts of Casablanca at the early design stage.In the scope of this study, Grasshopper-based digital workflow permits to investigate the impact of 147 parametric building designs, which are generated by varying the building's shape factor and orientation on thermal cooling and heating demand and global solar energy production. The outcomes of this holistic methodology highlight the design trade-offs between energy efficiency strategies and energy performance of building-integrated photovoltaic (bipv) and photovoltaic (pv) systems, aiming to reach optimized nZEB. Moreover, the results of the study suggest that it is possible to reach an annual load match equivalent to 29.68 %. The findings also underscore the significant role of the BIPV systems in shifting towards the goal of net zero energy, accounting for up to 64.43 % of the total solar energy output and contributing in total up to 17.62 % to the yearly self-sufficiency. In addition, the energy balance evaluation, when assessed on an hourly basis, reveals that the BIPV system significantly improves the daily load cover factor, achieving a value of 12.45 %, and increases up to 20.62 % when considering also the rooftop PV, particularly during spring season. Finally, the capacity credit factor is improved by up to 31.27 %, which is a significant share of grid connection reduction compared to the same building relying totally on the grid for its energy needs.

Power System Adequacy With Variable Resources: A Capacity Credit Perspective

Power System Adequacy With Variable Resources: A Capacity Credit Perspective

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

Capacity prioritization allocation and credit financing option in shipping freight forwarding market

Sudden external events always easily cause extreme capacity shortage and high freight rate. On this basis, shipping companies always dump containers from vulnerable forwarder when demand exceeds supply, which makes the capital-constraint forwarder under severe financial risk. Under this background, the freight market consisting of a shipping company and two forwarders with the differentiated capitals is considered to discuss the capacity priority strategy. Under this structure, the shipping company gives the priority to capital-abundant forwarder or capital-constraint forwarder, whereas the capital-constraint forwarder at the disadvantaged position also owns two options——trade credit financing or equity credit financing. From the results, only if the capacity is at a medium level will the shipping company prioritize the capital-constraint forwarder with trade credit financing; otherwise, who is prioritized has no effect on shipping company. In addition, the capital-abundant forwarder is not always in an advantageous position since the shipping company with dual role of seller and creditor will benefit more from the order of capital-constraint forwarder.

Cumulative Capacity Credit Estimation for Renewable Energy Projects

Cumulative Capacity Credit Estimation for Renewable Energy Projects

Efficiency-Driven Iterative Model for Underwater Compressed Air Energy Storage (UW-CAES)

The competitiveness of large-scale offshore wind parks is influenced by the intermittent power generation of wind turbines, which impacts network service costs such as reserve requirements, capacity credit, and system inertia. Buffer power plants smooth the peaks in power generation, distribute electric power when the wind is absent or insufficient, and improve the capacity factor of wind parks and their profitability. By substituting the variable pressure storage with an underwater variable volume air reservoir and reducing the wastage of compression heat using liquid Thermal Energy Storage (TES), which eliminates the combustor, the plant design allows overcoming the most common drawbacks of CAES plants. Underwater Compressed Air Energy Storage (UW-CAES) plants are investigated with a thermodynamic model to drive the power plant design toward efficiency maximization. Functional maps, constrained on the plant pressure ratio and the number of compressor/turbine phases with inter-refrigerated/inter-heating phases, are drawn by solving the model iteratively for the heat exchangers’ effectiveness to meet the target turbine discharge temperature, selected in advance to avoid unfeasible mathematical solutions.

Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit.

Analysis of Photovoltaic Plants with Battery Energy Storage Systems (PV-BESS) for Monthly Constant Power Operation

Photovoltaic generation is one of the key technologies in the production of electricity from renewable sources. However, the intermittent nature of solar radiation poses a challenge to effectively integrate this renewable resource into the electrical power system. The price reduction of battery storage systems in the coming years presents an opportunity for their practical combination with utility-scale photovoltaic plants. The integration of properly sized photovoltaic and battery energy storage systems (PV-BESS) for the delivery of constant power not only guarantees high energy availability, but also enables a possible increase in the number of PV installations and the PV penetration. A massive data analysis with long-term simulations is carried out and indicators of energy unavailability of the combined system are identified to assess the reliability of power production. The proposed indicators allow to determine the appropriate sizing of the battery energy storage system for a utility-scale photovoltaic plant in a planning stage, as well as suggest the recommended operating points made for each month through a set of graphs and indicators. The presence of an inflection zone has been observed, beyond which any increase in storage does not generate significant reductions in the unavailability of energy. This critical zone is considered the sweet spot for the size of the storage, beyond which it is not sensible to increase its size. Identifying the critical point is crucial to determining the optimal storage size. The system is capable of providing reliable supply of constant power in monthly periods while ensuring capacity credit levels above 95%, which increases the penetration of this renewable resource. Despite the fact that the study focuses exclusively on the analysis from an energy perspective, it is important to consider the constraints associated to real storage systems and limit their oversizing.

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.

Capacity Credit Variations of Photovoltaic Power Generation During its Life Cycle

At present, the evaluation results of capacity credit of photovoltaic (PV) power generation are mainly static fixed values that are independent of time. Considering the degradation of the performance of the PV module, the capacity credit should be a time-varying value rather than a static value during the life cycle of the PV power plant. In this paper, the annual performance degradation model of a PV power station is established, and the time-varying capacity credit in the life cycle of the PV plant is simulated by calculating the year-by-year capacity credit. By adding a 100MW PV system to the IEEE-RTS 79 system for a case study, the results are as follows: the PV capacity credit is 46.04% without considering module performance decay, 45.36% in the first year when degradation is considered, and only 31.96% at the end of the life cycle. The average capacity credit over the life cycle is about 38.38%. It shows that the capacity credit varies greatly during the life cycle of PV modules, and cannot be ignored in the evaluation of capacity credit.

Quantifying the capacity credit of IDC‐based demand response in smart distribution systems

AbstractWith the explosive growth of online services, the Internet Data Center (IDC) has become an emerging load demand . The power consumption of IDCs are believed suitable for demand response (DR) purposes due to its temporal‐spatial flexibility properties. To what extent IDCs could be used as candidate DR resources to provide capacity support are dependent both on the technical property and the willingness of data end‐users . To address the above issue, this paper presents a methodological framework for quantifying the potential value of IDC‐based DR in distribution grids. To achieve this, the concept of capacity credit (CC) is introduced and extended into the IDC scenario. The proposed framework explicitly considered the impacts of uncertainty of user participation intention and the reliability of basic information in the DR process. A novel Z‐number based price elasticity uncertainty model is introduced, and a more realistic evaluation of IDC capacity credit is obtained. Finally, the effectiveness of the proposed model and method is illustrated on a modified IEEE‐33 node network, and the obtained results verify the significance of IDC‐based DR in enhancing the adequacy of supply in distribution grids.

Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries

Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1–2 h forecast errors—resulting in early or delayed battery discharging relative to the onset of a reliability event—lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery’s availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.

Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications

High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.

The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum

Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite – Agent-based Simulation (EMIS-AS) model. We apply clean energy targets of 45%–100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the “right” market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non-incremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as capacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve-based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in capacity and operating reserve markets can impact deployment and operations, even for nonrestricted technologies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities.

The effect of short term storage operation on resource adequacy

The potential contribution of short term storage technologies such as batteries to resource adequacy is becoming increasingly important in power systems with high penetrations of Variable Renewable Energy Sources (VRES). However, unlike generators, there are multiple ways in which storage may be operated to contribute to resource adequacy. We investigate storage operational strategies which result in the same amount of Expected Energy Not Served (EENS) but differing Loss of Load Expectation (LOLE) to investigate the range of LOLE possible and what factors affect this range. A case study of a Belgium-like power system using an economic dispatch model, typical of state-of-the-art adequacy assessments, results in a LOLE ranging between 2 and 6 h/yr, with the difference decreasing for greater storage duration and increasing for higher installed capacities of storage. Capacity Credits (CCs), which give the relative contribution of a resource to system adequacy, may also be affected by storage operation and the CC of storage is shown to differ by up to 30% depending on the operation and how the CC is calculated. Given these findings, it is recommended that modellers be explicit and transparent about the storage operation they assume in adequacy assessments and capacity credit calculations.

Examination of the property, financial and profitability situation in the construction industry in Hungary: regional comparison

The construction industry is an important branch of the national economy, the structure of which in Hungary is characterized by strong fluctuations. In Hungary, as a result of the government's family support measures, construction companies have faced a significant increase in demand in recent years. This effect was uneven in different statistical regions of the country, but affected the property and financial condition of construction enterprises throughout the country. Starting from 2020, the COVID-19 epidemic has in several cases negatively affected the studied indicators of the construction industry. The article substantiates the need and methods of economic analysis of the state of the construction industry, investigates the property and financial condition of construction enterprises at the national level and in three less economically developed regions of Hungary. Similarities and differences in the values of indicators of the structure of assets, liquidity, profitability and efficiency of enterprises of this industry in certain regions compared to national indicators, as well as possible reasons for the existing state, are considered. The study covered the period of 2016-2020, and the statistical sample took into account the organizational and legal form, the size of assets, turnover and the number of employees of enterprises. Only those companies that worked continuously during the studied period were included in the statistical sample. On the basis of the conducted studies, it was established that the share of current assets in the assets of the investigated construction enterprises has a tendency to decrease. At the national level, the expansion of borrowing capacity and favorable credit conditions have resulted in significant indebtedness, although, in addition to bank loans, liabilities to suppliers have also played a large role. On the basis of the study, it was concluded that the three studied regions are characterized by very similar phenomena of a financial nature, but significant deviations from national values were found for several indicators (e.g. capital strength).

Firm capacity of PV+STG systems

The security of supply becomes a key variable of the electrical system. Due to the discontinuity of solar irradiance, PV generators are essentially not dispatchable and they operate only when there is solar resource but cannot ensure their availability when the energy is needed, so PV systems are considered having a null capacity credit. Energy storage is considered a key for the power sector and its sustainability and different options need to be exploited. The objective of this paper is analyse the optimum size of the required battery, its relations with the peak power of the generation system and the optimum operation setpoint of a PV+STG system for providing firm capacity