Electricity Generation Portfolio Research Articles

A new era for rural electric cooperatives: New clean energy investments, supported by federal incentives, will reduce rates, emissions, and reliance on outside power

This paper shows a least cost electricity generation portfolio for some of the largest rural electric cooperative utilities in the US. Due to the recent dramatic declines in renewable energy and battery storage costs, along with incentives under the federal Inflation Reduction Act (IRA) and excellent quality of renewable resource potential, we find that new investments in clean energy are significantly more cost-effective for most cooperative utilities than operating their existing coal and gas fired power plants. The study shows that rapid renewable energy (RE) deployment offers the rural cooperatives an opportunity to reduce their wholesale electricity costs by 10–20% compared with 2021 levels, while retiring their entire coal capacity by 2032. Most utilities could reduce their CO2 emissions by 80–90% relative to the 2021 levels, while also meeting load requirements at all hours, ensuring power supply reliability. While significant financing would be needed for such a transition to clean energy, we find that nearly half of the investments can be offset by the IRA tax credits. With bold and timely execution, cooperatives can reinvent their generation mix to provide affordable, reliable, and clean electricity that benefits rural communities.

Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.

Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty

Türkiye is the fastest-growing energy market among the OECD members, and it is among the world's top 20 energy-consuming countries. The existing electricity generation capacity of Türkiye has doubled in the last decade. Planning the electricity generation portfolio is an important problem for the decision and policymakers, especially in terms of sustainable development, and it is crucial for Türkiye's economic growth. In this paper, we study the long-term electricity generation portfolio optimization while considering the uncertainties. An optimization algorithm is modeled and solved while integrating uncertainty information as part of the Pareto concept. The results of three scenarios are presented and policy implications are discussed and compared with similar Turkish case studies and government projections. The results suggest that it is less risky to minimize emissions, thus, more likely to be sustainable than to minimize costs. Comparisons among solutions show that nuclear is a substitute for renewable resources and comparisons among other studies show that natural gas and hydro are substitutes for each other. Multi-objective formulation reveals that the government's scenario provides favorable results in business-as-usual conditions; however, nuclear and renewable investments that the government has planned are too ambitious to keep costs lower while decreasing emissions.

Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach

ABSTRACT The application of fuzzy multi-objective decision-making (F-MODM) to the optimization problems of renewable energy (RE) planning with uncertainties has become increasingly popular, although it is still rare in contrast to the use of traditional multi-attribute decision-making (MADM) methods. This study contributes to the domain of F-MODM modeling and the empirical applications of RE. Methodologically, as a recent trend to enhance the solution quality of fuzzy goal programming (FGP) in F-MODM is to introduce the “tolerance” (or admissible violation) concept to membership functions (MFs), this study proposes a novel ‘FGP-MT’ (multi-tolerance FGP) model to overcome the limitations of ‘single tolerance’ during FGP modeling (i.e. being unable to fit complicated ‘multitolerance’ decision contexts and running the risk of missing better optimality). The model is first verified using a numerical example from the F-MODM literature. Empirically, an application of the FGP-MT model is then considered for a SREPO (sustainable renewable energy portfolio optimization) decision, searching for the optimal electricity generation portfolio from multiple RE resources in Algeria for sustainable RE development, subject to the special preference structure of the decision-maker (DM) that involves manifesting two or more tolerances for a fuzzy goal. As the model performs well or even better in determining the most suitable SREPO decision under this condition, insights are gained for the studied case, and future applications of the model are therefore expected.

The co-benefits of California offshore wind electricity

California has set forth an ambitious goal of generating all its electricity from carbon-free technologies by 2045. Offshore wind (OSW) presents several attractive system, economic, and environmental attributes to help the state achieve these goals. Inclusion of OSW into the clean electricity generation portfolio could contribute significantly to total resource cost savings. In addition, OSW offers several major co-benefits. Its high and consistent capacity factor and generation time profile complements that of solar and helps enhance renewable electricity generation reliability. OSW could also be instrumental in early retirement of costly and pollution-heavy natural gas plants and lead to substantial job creations. Moreover, California could reap additional economic co-benefits from the development of a local wind energy industry. Additionally, OSW has the potential to advance environmental justice through reduction of ordinary air pollutants in urban areas and by bringing economic opportunities to lagging areas. At the same time, there are multiple challenges that must be addressed for OSW to reach its full potential. Our analysis is intended also to serve as a template for studies elsewhere by providing a comprehensive framework for estimating co-benefits, taking account of important local conditions, and identification of challenges and how they might be overcome.

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

As the share of global offshore wind energy in the electricity generation portfolio is rapidly increasing, the grid integration of large-scale offshore wind farms is becoming of interest. Due to the intermittency of wind, the stability of power systems is challenging. Therefore, accurate and fast offshore short-term wind speed forecasting tools play important role in maintaining reliability and safe operation of the power system. This paper proposes a novel hybrid offshore wind forecasting model based on swarm decomposition (SWD) and meta-extreme learning machine (Meta-ELM). This approach combines the advantages of SWD which has proven efficiency for non-stationary signals, with Meta-ELM which provides faster calculation with a lower computational burden. In order to enhance accuracy and stability, the signal is decomposed by implementing a swarm-prey hunting algorithm in SWD. To validate the model, a comparison against four conventional and state-of-the-art hybrid models is performed. The implemented models are tested on two real wind datasets. The results demonstrate that the proposed model outperforms the counterparts for all performance metrics considered. The proposed hybrid approach can also improve the performance of the Meta-ELM model as a well-known and robust method.

Decarbonization strategies for Switzerland considering embedded greenhouse gas emissions in electricity imports

Decarbonizing the energy system by electrification of heat and transport is only effective when using low-carbon electricity sources. As many countries such as Switzerland rely on imported electricity to meet their demand, the greenhouse gas (GHG) content of electricity imports must be correctly accounted for. By assuming an average GHG content for each amount imported, impacts of electricity required in peak periods are underestimated because additional (marginal) demand is primarily met with fossil power plants. This study employs a model to capture marginal GHG contents of imported electricity from a direct and indirect (life-cycle) perspective at an hourly resolution. Implications on GHG are explored for various electricity demand and supply scenarios including electrification of heat and transport, large-scale expansion of renewables, and nuclear phase-out. We find that depending on the scenario, the average GHG intensity of consumed electricity may double, while diurnal and seasonal variations are even larger. Nonetheless, results show substantial GHG mitigation of up to 45% with electrification in case of deploying a diversified electricity generation portfolio including photovoltaics and wind. For optimal GHG mitigation, short-term flexibility as provided by hydropower is necessary to manage electricity surpluses. The main challenge, however, surrounds seasonal energy storage including sector coupling.

Electricity Generation Portfolio Optimization Considering Cost, Emission, and Uncertainty: Turkish System Case Study

Electricity Generation Portfolio Optimization Considering Cost, Emission, and Uncertainty: Turkish System Case Study

Robust Flexible Unit Commitment in Network-Constrained Multicarrier Energy Systems

The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.

Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment

Although numerous studies have examined the economic benefits of demand response programs, the environmental impacts of such programs have been relatively underexplored. This study assesses the impact of demand resource bidding on the wholesale energy market and the environment, based on three years of high temporal-resolution data from Korea. In this demand resource bidding program, successful bidders were paid the system marginal price for reducing their electrical load at a given hour, which in turn reduced the generation of power from various technologies. This investigation of how carbon dioxide and particulate matter emissions from existing power systems changed with the introduction of the demand bidding program finds that the program altered the system operator's electricity generation portfolio and marginally abated carbon dioxide and particulate matter emissions from the power sector. It also shows that the environmental impact of the program varied over the course of the day and the year. The modest but statistically significant environmental impact of the demand resource bidding program points to the importance of including electricity demand resources in the discussion and development of energy and environmental policies for the power sector.

Flexible Ramping Services in Power Systems: Background, Challenges, and Procurement Methods

In recent years, the share of renewable energy sources (RES) in electricity generation portfolio has been growing, primarily supported by local and international energy policies to reduce the carbon footprint of the electricity sector. However, the large-scale integration of variable and uncertain RES has adverse effects on the power system operations. Therefore, the power system operators should apply advanced scheduling models and enhance the flexibility of the systems to compensate for the variability and uncertainty of RES. To deal with these challenges and covering the ramp scarcities and severe contingencies, not only more operational reserves are required but also the urgent actions is needed to develop the efficient markets, contracts, incentive mechanisms, effective scheduling models, and managerial approaches for encouraging suppliers to participate in flexibility procurement. This paper provides a background of the problem and a comprehensive review of the literature about flexible ramping markets and its procurement mechanisms.

Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios

The electricity sector has a key role to play in the sustainable energy transition. The falling costs of wind and solar PV have added to both the opportunities yet also challenges of balancing sometimes competing industry objectives of costs, security, and environmental impacts. This paper presents novel techniques for assessing possible future industry generation portfolios in three ways: (1) incorporating explicit metrics for energy trilemma objectives into modelling, (2) using the optimization process of evolutionary programming to map the solution space of ‘high performing’, near least-cost, portfolio solutions, and (3) applying boundary min–max cases and clustering to categorize these varied portfolios to better facilitate planning and policy making. We use an open-source evolutionary programming tool, National Electricity Market Optimiser, to assess possible future generation portfolios for Indonesia’s Java-Bali interconnected power system. Our findings highlight the wide range of possible portfolios that might potentially deliver similar total industry costs, and their different security and environmental implications. In particular, additional solar photovoltaic deployment appears a low-risk opportunity to reduce costs and emissions compared to more fossil-fuel oriented mixes. Our novel techniques may be useful for the energy modelling community seeking to better understand and communicate complex, uncertain, and multi-dimensional choices for electricity industry planning.

Optimization of Lebanon's power generation scenarios to meet the electricity demand by 2030

Presently, Lebanon provides 95 % of the primary energy electricity power generation by using fuel-oil used in thermal power plants. To meet the population needs, private generators are also used in all the country and they represent the third of total electricity production. The challenges over the future development of the Lebanese electric sector are economic and environmental. This is why currently, the energy policy makers aim to diversify the national electricity generation mix in the energy planning strategy and to introduce low-carbon technologies. The complexity of these challenges in the particular Lebanese context has motivated our study which aims to recommend policies to develop the optimal electricity generation scenario by 2030. This paper presents a methodology to evaluate different scenarios reflecting different combinations of technologies by 2030. This is achieved by using an excel tool “Excel Solver Optimization Calculator” which makes possible the interaction of various inputs to produce a least-cost generation mix. The main results focus on the least-cost electricity generation portfolio, total investment required to generate electricity, level of energy independence and carbon emissions. Many policy choices could be feasible and very advantageous for Lebanon if renewable energies are deployed massively. However, this requires policies that support the massive use of renewable energy technologies in the mix.

Resolving conflict objectives between environment impact and energy efficiency – An optimization modeling on multiple-energy deployment

Energy consumption increases dramatically due to economic development, leading to the raising of toxic materials such as CO2 during electricity generation process. This study aims at solving the electricity generation portfolio issue to maximize electricity generation while minimizing the CO2 emission to protect the environment. In this research, multiple types of power plants to produce electricity, such as by gas, coal, solar, are alternatives for electricity generation portfolio. Especially, uncertainty in electricity demand is included and dealt by a stochastic chance constrained method. Further. The proposed model is solved by a non-dominated sorting genetic algorithm for multiple objectives. The result presents the optimal capacity portfolio plan for electricity generation and suggests a trade-off decision between conflicting goals to balance electricity generation efficiency and environmental protection.

Energy Transition and EERS: A Viable Policy Measure to Demand-side Management

본 논문은 에너지 전환을 효과적으로 추진하기 위해서는 전력공급 차원만 강조하는 것이 아니라 수요관리를 포함해서 사고해야 하다는 문제의식 하에 한국적 상황에서 효과적인 수요관리의 수단으로 에너지효율향상의무화제도를 분석한다. 본 논문은 첫째, 수요관리를 에너지 전환의 사고 속에 포함하기 위해 전력원 구성에서 에너지효율화(EE)를 포함해야 한다고 주장한다. 둘째, 에너지효율향상의무화제도(EERS)를 도입할 때 에너지 전환에 어떤 기여를 할 것인가에 대한 시물레이션 결과를 제시함으로써 수요관리 중에서도 EERS에 주목해야 한다고 주장한다.This article analyses the effect of EERS as a viable policy measure to demand-side management(DSM) amidst the national energy transition in Korea. The findings of the study suggest that demand reduction through energy efficiency(EE) should be included in the electricity generation portfolio in order to correctly capture and intuitively understand the impact of energy efficiency measures in long-term electricity supply plan. Moreover, the simulated impact of EERS to the electricity demand shows that EERS can be the most effective methods among many available EE practices if properly implemented.

Morocco\u2019s sustainable energy transition and the role of financing costs: a participatory electricity system modeling approach

BackgroundMorocco is facing major challenges in terms of its future energy supply and demand. Specifically, the country is confronted with rising electricity demand, which in turn will lead to higher fossil fuel import dependency and carbon emissions. Recognizing these challenges, Morocco has set ambitious targets for the deployment of renewable energy sources for electricity generation (RES-E). The realization of these targets will lead to a fundamental transition of the Moroccan electricity sector and requires substantial public and private investment. However, different risks constitute barriers for private RES-E investments and lead to high financing costs, which may eventually discourage capital-intensive RES-E projects.MethodologyWhile the existing literature has mainly focused on assessing the impact of financing costs on the economic competitiveness of individual technologies, the aim of this research is to assess the techno-economic feasibility of different electricity generation portfolios. To recognize the social dimension of the sustainable energy system transition, the electricity scenarios for Morocco have been jointly developed with stakeholders in a scenario building workshop in Rabat, employing a downscaled version of the open source electricity market model renpassG!S, augmented by a weighted average cost of capital (WACC) module.ResultsIn the stakeholder workshop, four different electricity scenarios for Morocco were co-developed. Each of these scenarios describes a consensual and technologically feasible future development path for the Moroccan energy system up to 2050, and comprises conventional fossil fuel-based technologies, as well as RES-E technologies in varying shares. Employing the downscaled renpassG!S model, we find that total system costs, as well as average levelized costs of electricity (LCOE) can be reduced substantially with low-cost financing.ConclusionsOur results indicate that de-risking RES-E investments can lead to cost competitiveness of a 100% RES-E-based electricity system with mixed-technology scenarios at marked financing costs. Therefore, we identify specific de-risking recommendations for Moroccan energy policymaking. In addition, we argue that participatory scenario modeling enables a better understanding of the risk perceptions of stakeholders, and can eventually contribute to increasing the political feasibility of sustainable energy transition pathways.

Impacts of electric vehicles on the electricity generation portfolio – A Scandinavian-German case study

This study investigates how electrification of the Scandinavian and German road transportation sectors under a stringent CO2 cap will influence investments in new electricity generation capacity up to Year 2050 and the dispatch of the electricity generation portfolio in Year 2030. We apply a cost-minimisation investment model and an electricity dispatch model of the Scandinavian and German electricity systems, assuming both optimised charging and a vehicle-to-grid (V2G) charging strategy for passenger electric vehicles (EVs). Different EV battery sizes and EV deployment levels are investigated in 11 different scenarios, whereby two of the scenarios include also electric trucks and buses using electric road systems (ERS). The results of the modelling show that with a cap on CO2 emissions, the additional electricity demand from an electrified road transport sector is met mainly by increases in the outputs from wind power and thermal power plants, in the form of coal in combination with carbon capture and storage. In Year 2030, wind power generation in Scandinavia and Germany increases by 7–30% depending on the EV scenario, as compared to a scenario without EVs, which corresponds to a few more percentage points than the increased demand from EVs in absolute terms. Furthermore, a V2G charging strategy for passenger EVs smoothens the net load curve and almost completely reduces the need for peak power capacity in the Scandinavian-German electricity system. The value of investing in solar power is also reduced in all the EV scenarios by 22–42%, as compared to a scenario without EVs. This is due to the fact that in Northern Europe solar power competes with EVs for peak power supply. ERS for mainly trucks and buses will increase the current load profile by up to 18 GW in the Scandinavian-German electricity system.

How Electric Vehicles and the Grid Work Together: Lessons Learned from One of the Largest Electric Vehicle Trials in the World

In the coming years, hundreds of thousands of new electric vehicles (EVs), from plug-in hybrids to fully electric, will hit the roads around the world, adding to the current EV fleet of more than 2 million, according to the Global EV Outlook 2017. The electrification of transportation can bring environmental, health, and economic benefits when coupled with a low-carbon electricity generation portfolio; however, ensuring that this transition goes smoothly requires addressing several grid-integration challenges.

Hydro-Optimization-Based Medium-Term Price Forecasting Considering Demand and Supply Uncertainty

This paper proposes an electricity market model of Turkish electricity market for monthly and yearly electricity price forecasting in medium-term by means of supply and demand dynamics formed via a theoretical approach. The electricity market model created within this scope consists of three main components related to electricity demand, supply, and price segments along with hydro optimization submodel, which takes into account the nonlinear relation between supply and price. Electricity price is determined based on the intersection of demand curve and merit order curve that has dynamic behavior for dam-type hydrogeneration, import coal, and natural gas power plants. The paper aims to determine the range of possible electricity prices rather than a single price forecast by creating multiple scenarios based on the uncertainties in main variables affecting the electricity prices. Meanwhile, electricity generation portfolio with respect to market participants and primary energy resources as well as price forecasts can be obtained simultaneously. Ultimately, the model can identify how effective a variable of the market on the electricity price is. The developed method is validated via real data.