Mix Of Generation Technologies Research Articles

Crises in Texas and Australia: Failures of energy-only markets or unforeseen consequences of price caps?

The Australian National Electricity Market (NEM) and the Texas wholesale electricity market (ERCOT) are two of the most prominent examples of energy-only wholesale electricity markets in the world. In early 2021 and mid 2022 these markets suffered from unprecedented crises, prompting some commentators to question the energy-only market design. Both markets are now considering implementing a form of capacity mechanism. Are these crises evidence of a fundamental flaw in the energy-only market design, or something else? We argue that, although the crises were very different in form, both crises arose in part from the effect of price caps in the wholesale market. We set out a model of the optimal mix of generation technologies in a framework in which generators can invest to protect against extreme weather events. We show that a price cap (below VoLL) reduces incentives for investment in hardening generation. Rather than further lowering the price caps – as was done in Texas – we propose a range of reforms to strengthen and enhance the confidence in the wholesale market.

Avoiding ecosystem and social impacts of hydropower, wind, and solar in Southern Africa’s low-carbon electricity system

The scale at which low-carbon electricity will need to be deployed to meet economic growth, electrification, and climate goals in Africa is unprecedented, yet the potential land use and freshwater impacts from this massive build-out of energy infrastructure is poorly understood. In this study, we characterize low-impact onshore wind, solar photovoltaics, and hydropower potential in Southern Africa and identify the cost-optimal mix of electricity generation technologies under different sets of socio-environmental land use and freshwater constraints and carbon targets. We find substantial wind and solar potential after applying land use protections, but about 40% of planned or proposed hydropower projects face socio-environmental conflicts. Applying land and freshwater protections results in more wind, solar, and battery capacity and less hydropower capacity compared to scenarios without protections. While a carbon target favors hydropower, the amount of cost-competitively selected hydropower is at most 45% of planned or proposed hydropower capacity in any scenario—and is only 25% under socio-environmental protections. Achieving both carbon targets and socio-environmental protections results in system cost increases of 3-6%. In the absence of land and freshwater protections, environmental and social impacts from new hydropower development could be significant.

Strategizing towards sustainable energy planning: Modeling the mix of future generation technologies for 2050 in Benin

The Benin energy sector faces serious challenges, including an unfavorable energy mix with regular power shortages, erratic power outages, reliance on electricity imports, and dependence on traditional cooking stoves. This study has investigated strategies critical for Benin to employ to achieve 24.6 %, 44 %, and 100 % renewable energy (RE) integration targets in the final electricity mix in 2025, 2030, and 2050, respectively. This study used the EnergyPLAN model to develop different energy scenarios suitable for Benin to achieve its proposed RE penetration target. A combination of natural gas (NG) with solar photovoltaic (PV), wind energy, hydropower, and concentrated solar power (CSP) is used to develop three scenarios for RE integration namely the government targets scenario, 2 % RE per year scenario and 50 % RE in 2050 scenario. The results show that the government targets scenario is too ambitious because of the current trend and pace of developing the energy sector. Moreover, a combination of 563 MW of NG, 125 MW of PV, 200 MW of wind, 600 MW of hydropower, and 60 MW of CSP would achieve 50 % RE by 2050 under the 50 % RE scenario. This scenario would decrease CO2 emissions by 50 % with no CEEP generation. Furthermore, the total electricity generation from MSW in Benin is estimated to be 0.232, 0.3215, and 1.16 TWh/yr in 2025, 2030, and 2050, respectively. The study's findings could help decision-makers and stakeholders make informed decisions to promote the integration of RE resources in the Benin Republic.

Transition to Renewable Energy: An Attempt to Model the Mix of Existing and Future Generation Technologies for 2035 and 2050

Transition to Renewable Energy: An Attempt to Model the Mix of Existing and Future Generation Technologies for 2035 and 2050

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

Application of modern portfolio theory to the European electricity mix: an assessment of environmentally optimal scenarios

The proposed study analyzes the efficiency of the European energy mix of electricity generation technologies from two perspectives: environmental and economic. The context is that of European energy dependence and a technology mix conditioned by the import of fossil fuels. The impact is centered, among other elements, on the leakage of national income and the emission of polluting gases. The aim is to determine the participation that each type of power plant in each country should present in order to minimize the total environmental impact. In order to solve this problem, a double optimization is proposed through the use of two methodologies: one based on a multi-criteria decision-making method (MCDM) model with which to evaluate the environmental performance of each power plant, and a second optimization based on a quadratic model of portfolios modern portfolio theory (MPT), with which to evaluate the efficiency of the portfolio of technologies from the cost/risk binomial. The results confirm that an environmentally efficient portfolio leads to higher levels of economic risk-taking, with a slight increase in the level of assumed cost. Nevertheless, from the results obtained, it is possible to say that hydro (with a share between 11 and 13%) and wind (37–44% mix participation) technologies resulted to be preferred options both environmentally and in terms of minimum risk efficiency. Nuclear power generation stands out as one of the main baseload technologies with shares between 25 and 35% in environmental and cost/risk efficiency. As main findings, Hydro, supercritical lignite, solar PV and wind are identified as preferential technologies to be present in both minimum risk and minimum cost efficient portfolios. In case European Union pursues for minimizing the cost of electricity production, the shares of supercritical lignite, nuclear, solar PV and wind energy go up to the maximum allowed limits. The novelty lies in the application of both methodologies with which to complement the analysis and design efficient portfolios of energy technologies from environmental and economic points of view. One possible future approach would be to assess (with the MPT model) new environmentally optimal portfolios obtained through the application of other MCDM techniques.

Long Term Planning of Macedonian Electricity Supply

In the paper alternatives for expansion of the Macedonian electricity supply system are presented. MESSAGE tool is used for modelling the system, for developing the scenarios, to analyse cost optimal energy pathways and to determine the optimal electricity generation technology mix. The calculations for the period 2020-2050 are made in a 5-year interval. Two scenarios are developed: Scenario 1 – BAU (Business as Usual) scenario, where the current energy structure prevailed, with trends like the current development. The electricity demand is satisfied by coal-fired thermal power plants, gas-fired thermal power plants, hydro power plants and renewable power plants. Scenario 2 – Green scenario, where gas-fired thermal power plants and nuclear power representative with small modular reactors are base load technologies, and also intensive construction of production capacities on renewable energy sources is forced. By making further analysis, the possibility of flexibility in the construction of new facilities within the interval, economic indicators, and appropriate impact on the environment are obtained. In order to make an economic analysis for a particular technology, calculations are made for parameters that are standard for projects, such as: net present value, benefit – cost ratio, payback period and internal rate of return. The environmental impact is analysed of aspect of CO2 emissions.

A change in the air? The role of offshore wind in Australia’s transition to a 100 % renewable grid

Rapid decarbonisation of electricity production is required if Australia is to meet its obligations under the Paris Agreement. Critical to achieving this at low cost while maintaining system reliability is the selection of an appropriate mix of generation technologies to service electrical demand. Australia has seen extensive deployment of renewable energy technologies such as onshore wind and solar but has not yet seen the adoption of offshore wind technology. However, there is currently significant interest in developing this resource, with ongoing debate occurring about future technology costs and the potential of onshore renewables to meet electrical demand.This article presents the results of an investigation into the techno-economic impact of exogenously fixing offshore wind capacity on a future least-cost Australian National Electricity Market with 100 % renewable generation. An existing open-source cost optimisation model, National Electricity Market Optimiser, was used for the study.It was found that increasing the capacity of offshore wind in the generation mix leads to displacement of both onshore wind and solar generators. This is due to the greater magnitude and consistency of the offshore wind resource relative to onshore. Increasing offshore wind capacity therefore tends to reduce the total system generation capacity, as well as the amount of unused surplus generation. Using lowest published projections for future capital costs, inclusion of offshore wind was found to reduce total system costs. Using an average of future cost projections, total system costs were found to increase. However, adding up to 15 GW of offshore wind capacity to a 100 % renewable system would only impact total system costs by 5 %. Given the other potential advantages of offshore wind, namely closer siting to load centres, reduced need for onshore land resources, and the potential to transition existing fossil fuel workers, our results suggest that offshore wind may be a suitable candidate for inclusion in Australia’s transition to a low carbon electricity system, under a range of future cost scenarios.

BIGQUERY FOR BIG DATA ANALYSIS

In the proposed paper scenarios for expansion of the Macedonian electricity generation system are investigated. In order to determine the optimal electricity generation technology mix sufficiently enough to feed the country electricity demand MESSAGE tool is used. The planning period for the case study is from 2020 (base year) to 2050. The environmental impact, for each scenario, is also analyzed of aspect of CO 2 emission.

Power balancing capacity and biomass demand from flexible district heating production to balance variable renewable power generation

The European Commission has, following the Paris Agreement, announced a “European Green Deal” to decarbonize energy sectors and increase renewable power. This study investigates to what extent district heating systems with biomass-fueled combined heat and power, electricity driven compression heat pumps, and pit thermal energy storages, can contribute to power balancing capacity in a future Swedish power system with a high share of variable renewable electricity production. District heat production is, in this study, unconventionally controlled to primarily supply a power balancing demand, where co-produced heat is stored if not directly supplied to district heating users. The impact of this on the biomass demand is also investigated. Simulations are made on an aggregated level for one part of the Swedish electricity market. The results show that district heating systems have the potential to reduce peak variable renewable power deficits by up to 52%. All power surpluses can potentially be used for heat production in heat pumps. A heat storage capacity of 17–18% of the heat demand is necessary. Fuel use are 11–12% higher for district heating production controlled for power balancing compared to conventional heat production, depending on the mix of renewable power generation technologies. For instance, a large share of solar power in relation to wind power reduce fuel use to a greater extent when compared to the opposite relation.

Demystifying natural gas distribution grid decommissioning: An open-source approach to local deep decarbonization of urban neighborhoods

In this paper, deep decarbonization in an urban neighborhood in Vienna, Austria is proposed focusing on decommissioning of the gas distribution grid for heat supply rather than trying to feed in “green” gas in the future. The core objective is to demonstrate that alternative network infrastructures and energy technologies ensure not only an adequate but also an even superior provision of local heat energy services. Two different deep decarbonization pathways are studied, namely, electrification of almost all energy services and expansion of the district heating network. In addition, future district cooling service supply is considered. The method applied couples and extends two open-source models offering a complete analysis toolkit covering a high spatial and temporal resolution. The results show that deep decarbonization of local multiple-energy carrier systems is possible, without being dependent on the existing distribution grid of natural gas. Possible stranded assets (also at the gas end-user level) must not play a decisive role, especially since the trade-off analyses in this work show that alternative scenarios of lower/zero-emission energy service provision are even more economical in the longer term since the CO2 price is expected to increase in the next decades. Future work may focus, among others, on the energy generation technology mix feeding into the district heating grid, the local mobility service needs, and a higher granularity to improve the assessment of the on-site (building-integrated) renewable generation potential associated with the emergence of energy community concepts.

An Analysis of the Impacts of Solar Electricity Generation on the Electricity Industry for Thailand

This paper provides an analysis of the impacts of solar electricity generation on the electricity industry for Thailand. In this paper, three scenarios (REF, Solar2015 and Solar2018) are developed to represent an increased levels of electricity produced from solar energy. A Low Emissions Analysis Platform (LEAP) model is employed in this paper to assess the impacts for the period 2019-2037. This paper assesses the scenario impacts in terms of diversification of electricity generation, generation technology mix, primary energy requirement and CO2 emissions. The analysis reveals that greater role of solar energy in the electricity generation would have positive impacts on the Thai electricity generation from several aspects including improving the diversification of primary energy supply for electricity generation, reducing fossil fuel consumptions for power production, less reliance on fossil fuel sources and environmentally friendly electricity generation. For example, 20% share of solar in fuel mix for power generation would result in a 17% reduction in fossil fuel consumption. Furthermore, it would contribute to a 16% decrease in CO2 emissions and 28% reduction in SO2 emissions. Despite several benefits, there are a number of emerging barriers for promoting solar based electricity generation in Thailand. These include inefficient and unreliable nature of solar energy for baseload electricity demand, high-initial cost, unsupportable grid infrastructure and unfavourable regulatory framework. This paper, therefore, recommends that development of energy storage technologies, improvement of grid flexibility and the revision of the regulations to support solar energy business could be effective strategies in order to address the barriers facing the Thai electricity industry.

Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector

Carbon dioxide removal technologies, such as bioenergy with carbon capture and direct air capture, are valuable for stringent climate targets. Previous work has examined implications of carbon removal, primarily bioenergy-based technologies using integrated assessment models, but not investigated the effects of a portfolio of removal options on power systems in detail. Here, we explore impacts of carbon removal technologies on electric sector investments, costs, and emissions using a detailed capacity planning and dispatch model with hourly resolution. We show that adding carbon removal to a mix of low-carbon generation technologies lowers the costs of deep decarbonization. Changes to system costs and investments from including carbon removal are larger as policy ambition increases, reducing the dependence on technologies like advanced nuclear and long-duration storage. Bioenergy with carbon capture is selected for net-zero electric sector emissions targets, but direct air capture deployment increases as biomass supply costs rise.

Emissions of electric vehicle charging in future scenarios: The effects of time of charging

AbstractElectrification of transport is an important option to reduce greenhouse gas emissions. Although many studies have analyzed emission implications of electric vehicle charging, time‐specific emission effects of charging are inadequately understood. Here, we combine climate protection scenarios for Europe for the year 2050, detailed power system simulation at hourly time steps, and life cycle assessment of electricity in order to explore the influence of time on the greenhouse gas emissions associated with electric vehicle charging for representative days. We consider both average and short‐term marginal emissions. We find that the mix of electricity generation technologies, and thus, also the emissions of charging, vary appreciably across the 24‐h day. In our estimates for Europe for 2050, an assumed day‐charging regime yields one‐third‐to‐one‐half lower average emissions than an assumed night‐charging regime. This is owing to high fractions of solar PV in the electricity mix during daytime and more reliance on natural gas electricity in the late evening and night. The effect is stronger during summer months than during winter months, with day charging causing one‐half‐to‐two‐thirds lower emissions than night charging during summer. Also, when short‐term marginal electricity is assumed, emissions tend to be lower with day charging because of contributions from nuclear electricity during the day. However, the results for short‐term marginal electricity have high uncertainty. Overall, our results suggest a need for electric vehicle charging policies and emission assessments to take into consideration variations in electricity mixes and time profiles of vehicle charging over the 24‐h day.

Investing in generation and storage capacity in a liberalised electricity market: An agent based approach

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions.

Optimizing design and dispatch of a renewable energy system

Renewable energy technologies are becoming increasingly important due to their cost-competitiveness, and because of enhanced climate concerns. We demonstrate the capabilities of an integer-programming optimization model that minimizes capital (investment) and operational costs, and utility charges, while adhering to system sizing constraints, demand requirements, and interoperability characteristics of the systems chosen. The model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies, while simultaneously optimizing the corresponding dispatch strategy. Our case studies explore several venues, i.e., a small campus and a local hospital, with complex utility rate tariffs, multi-technology integration opportunities, and incentives for renewable power production. Using an optimization model, versus applying rules of thumb, can produce millions of dollars in savings over a 25-year time horizon and result in thousands of kilowatts of installed renewable energy.

An iterative approach for optimal decarbonization of electricity and heat supply systems in the Great Britain

The electrification of heat supply is a widely discussed strategy for decarbonizing the heat sector in the Great Britain (GB). This impacts the electricity load duration curve and affects the optimal mix of power generation technologies. Additionally, the price of electricity and the emission from the grid determine whether the electrified heat is cost-effective and low carbon. These interdependencies necessitate adopting an integrated approach for long term planning of heat and electricity supplies to ensure cost-effective decarbonization. In this work, we have developed an iterative approach for investigating optimal mix of technologies in electricity and heat sectors considering the interactions between these sectors. This approach was applied to GB as a case study. Firstly, the capacity and operation of various technologies for electricity generation were determined to supply electricity demand (including electricity demand for heating) with a minimum annualized cost. Then, using the levelized cost of electricity calculated in the power generation mix optimization problem, the optimal heat supply mix was determined through the minimization of annualized cost. The iterative optimization of electricity and heat was continued until an equilibrium was achieved. The results were compared with a centralized optimization model that heat and electricity supply problems are solved simultaneously.

Learning is not enough: Diminishing marginal revenues and increasing abatement costs of wind and solar

The economics of wind and solar generation face two opposing drivers. Technological progress leads to lower costs and both wind and solar have shown dramatic price reductions in recent decades. At the same time, adding wind and solar lowers market electricity prices and thus revenue during periods when they produce energy. In this work, we analyze these two opposing effects of renewable integration: learning and diminishing marginal revenue, investigated using a model that assumes the status quo with regards to generation technology mix and demand. Our modeling results suggest that reduction in revenue from market forces may offset or even outpace technological progress. If deployed on current grids without changes to demand response, storage or other integrating technologies, the cost of mitigating CO2 with wind will increase and will be no cheaper in the future than it is today for solar. This study highlights the need to deploy grid technologies such as storage and new transmission in order to integrate wind and solar in an economically sustainable manner.

Assessing the optimal generation technology mix determination considering demand response and EVs

This paper proposes a novel, generation technology mix determination method considering short term demand response, energy storage systems, and electric vehicles to provide more flexibility to the future power systems. In the proposed method, new models of both electric vehicles and energy storage technologies for contributing to generation mix determination studies are suggested. The integration of the different emerging technologies, i.e. demand response, energy storage systems, and electric vehicles into traditional generation mix determination is done firstly by adopting mix complementary programing method. Then, to overcome the problems of such integration and to avoid its complexity, it is converted to quadric complimentary programing model. The proposed generation mix determination framework is tested on the Spanish power system with real data. The outputs of the proposed method are the optimal capacities of the conventional generating units, the different types of energy storages and wind turbines. Simulation results demonstrate the effectiveness of the proposed method in determining the optimal generation mix of future power systems with high penetration level of wind energy resources. Moreover, the results verify the potential of the proposed method in providing better flexibility services to power system if compared with other methods.

Generation expansion planning with high shares of variable renewable energies

Worldwide, the utilization of Renewable Energies (REs) for electricity generation is growing rapidly driven by the increasing fears of fossil fuels depletion, the price volatility of these fuels and the necessity of reducing the Green House Gas (GHG) emissions to preserve the environment. On the other hand, REs especially the Variable Renewable Energies (VREs) like wind and solar power suffer from intermittency in its output generation. This intermittency can introduce severe technical and economic problems for the power systems with high penetration from these energies. This intermittency should be mitigated not only during the system operation phase but also during power system planning phase. For this purpose, the classical power system planning methodologies and models should be upgraded to account for this intermittency in a way to find the optimum solutions to mitigate it. In this regard, this paper will focus on developing a new Generation Expansion Planning (GEP) model to find the optimum mix of dispatchable generation technologies that can allow the integration of VREs into the power system while mitigating the technical and economic impacts of its intermittency. In addition, a number of new concepts related to generation mix flexibility, VREs capacity credit and role of system operating reserve in integrating VREs will be revisited. Then, the developed GEP model will be applied to a case study handling the future expansion scenarios of VREs in the Egyptian grid. Results obtained show that, increasing the share of VREs in the grid will shift the mix of new generation capacities from the least cost and low flexibility options into more expensive and flexible generation options.