Generation Adequacy Research Articles

Model coupling and comparison on optimal load shifting of battery electric vehicles and heat pumps focusing on generation adequacy

The energy transition fosters a dynamic landscape marked by renewable energy, electrification, and complex interactions among actors and technologies. Employing model experiments and comparisons shows promise for exploring these connections and enhancing model clarity and precision. This study adopts a multi-model approach, integrating a model comparison to probe how the electrification of demand-side sectors and strategic load shifts of battery electric vehicles and heat pumps might impact Germany's generation adequacy by 2030. Specific demand models from the transport and heating sectors and a future load structure projection model are interlinked with three electricity system models. The comparative analysis of the three electricity system models unveils discrepancies in dispatch decisions for power plants, flexibility options' load shifts, and their effects on generation adequacy, directly tied to model attributes.The comparison underscores methodological variations (linear optimization versus agent-based simulation, myopic foresight versus perfect foresight) as pivotal, emphasizing the significance of considering load change and start-up costs for power plants. The results show that with optimized load shifting by electric vehicles and heat pumps, the adequacy of power generation is less strained despite increased electricity demand. Moreover, load shifts mitigate curtailment of renewables and consumers, reducing carbon emissions by lowering conventional power generation.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Public policies to foster green hydrogen seasonal storage: Portuguese study case model until 2040

Portugal made a climate commitment when it ratified the Paris Climate Agreement in 2015. As a result, Portugal, along with other EU members, has created a national roadmap for the deployment of hydrogen as a crucial component of Portugal's energy transition towards carbon neutrality, creating synergies between the electric and gas systems.The increased variability of generation from variable renewable power sources will create challenges regarding the security of supply, requiring investment in storage solutions to minimize renewable energy curtailment and to provide dispatchability to the electric power system. Hydrogen can be a renewable energy carrier capable of ensuring not only the desired transformation of the infrastructures of the gas system but also an integrator of the Electric System, such as in Power-to-Power (P2P) systems. Hydrogen can be produced with a surplus of renewable electricity from wind and solar, allowing a long-term energy seasonal storage strategy, namely by using underground salt caverns, to be subsequently transformed into electricity when demand cannot be supplied due to a shortage of renewable generation from solar or wind.P2P investments are capital intensive and require the development of transitional regulation mechanisms to both create opportunities to market agents while fostering the energy surplus valuation and decreasing the energy dependency. In order to maintain the electric system's security of supply, the suggested methodology innovatively manages the importance of seasonal storage of renewable energy surplus using hydrogen in power systems. It suggests a novel set of regulatory strategies to foster the creation of a P2P solution that maintains generation adequacy while assisting in decarbonising the electric power industry. Such methodology combines long-term adequacy assessment with regulatory framework evaluation to evaluate the cost of the proposed solutions to the energy system.A case study based on the Portuguese power system outlook between 2030 and 2040 demonstrates that the considerable renewable energy surplus can be stored as hydrogen and converted back into electricity to assure adequate security of supply levels throughout the year with economic feasibility under distinct public policy models.

Gas power — How much is needed on the road to carbon neutrality?

The national and international expansion of renewable energy and the retirement of conventional capacity are increasing the challenge of ensuring generation adequacy in the European power system. New gas-fired power plants have been perceived as a promising option to secure supply. However, recent geopolitical developments have raised concerns about the high dependence on natural gas. This paper explores how different scenarios of power consumption and renewable energy targets in Germany determine the reliance on gas-fired power generation capacity in 2030, 2040, and 2050. Using the electricity market model ELTRAMOD it examines the effects of the different scenario assumptions on gas-fired capacity and generation, renewable capacity, electricity trade, and greenhouse gas emissions in Germany and abroad. The results demonstrate a clear need for reliable capacity in the future, with gas-fired capacity accounting for the largest share. In all scenarios analyzed, Europe’s dependence on natural gas increases from historical levels until 2030 and then falls only slightly below historical levels until 2040. Stricter German renewable energy targets shift natural gas use from Germany to other European countries. Phasing out coal in Germany before 2030 instead of in 2038 makes Germany dependent on reliable capacity from abroad and increases its reliance on natural gas.

Distributed generation as efficient measure to improve power generation adequacy

Distributed generation as efficient measure to improve power generation adequacy

Impact analysis of electricity pricing on the reliability of integrated Nepal power system

Abstract Time of Use of Electricity Pricing (TOU) strategy is adopted by many countries to reduce the peak load and minimize the cost of Electricity. In this research, the effect of the TOU tariff system on the reliability of the Integrated Nepal Power System (INPS) has been investigated. Implemented TOU is based on three time periods: Peak time, Flat time, and Valley time (PFV). The moving variable method is used to find the optimal PFV period in a day. Particle swarm optimization (PSO) is used to find optimal prices corresponding to the given periods, where two objective functions are converted to a single objective function. Prices are modeled with the demand response and a new load pattern of consumers after TOU has been obtained. Power plants of INPS are modeled into four states model and the Monte-Carlo simulation is used to find the generation adequacy indices like Loss of Load Probability (LOLP), Loss of Load Expected (LOLE), and Loss of Energy Expected (LOEE) before and after the implementation of the TOU. The risk level of INPS is reduced by 37% and peak load is reduced by 4.2% after the implementation of TOU. It is found that the reliability of INPS can be improved with the help of TOU

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.

Reacting to changing paradigms: How and why to reform electricity markets

The energy crisis and the accelerated transition to climate neutrality result in a shift from the traditional energy trilemma to an “energy quartet” across Europe. Firstly, the criteria of affordability, previously focused on short-term price developments, broadens to reliable affordability including in crises. Secondly, clean energy traditionally focused on clean production now extends to clean energy systems with climate neutral provision of flexibility and enhanced energy efficiency. Thirdly, generation adequacy extends to energy system resilience, for example to interruptions of fuel supply. Fourthly, a new objective of reliable speed is emerging, responding to the constrained carbon budget and the need for predictable demand for energy technologies to unlock investments in manufacturing capacity. Synergies can be realized by jointly addressing the energy quartet. First, hedging of both consumers and producers against power costs (but not price) fluctuations by passing on the conditions of contracts for differences to consumers can enhance speed and predictability of renewable investments. Second, nodal pricing allows the demand side to resolve rather than contribute to transmission constraints, enhancing system security and reducing emissions from fossil assets, which are currently dispatched to resolve transmission constraints. Third, a reformed strategic reserve can contribute to reliable affordability and system security.

Optimizing generating unit maintenance with the league championship method: A reliability-based approach

The electrical power industry has experienced an unprecedented pace of digital transformation as a prevailing economic trend in recent years. This shift towards digitalization has resulted in an increasing interest in the collection of real-time equipment condition data, which provides opportunities for implementing sensor-driven condition-based repair. As a result, there is a growing need for the development of generator maintenance scheduling to consider probabilistic equipment behavior, which requires significant computational efforts. To address this issue, the research proposes the use of a meta-heuristic league championship method (LCM) for generator maintenance scheduling, considering random generation profiles based on generation adequacy criteria. The experimental part of the study compares this approach and its modifications to widely used meta-heuristics, such as differential evolution and particle swarm methods. The identification and demonstration of optimal method settings for the generation maintenance scheduling problem are presented. Subsequently, it is illustrated that employing random league scheduling expedience can reduce the variance of objective function values in resulting plans by over three times, with values of 0.632 MWh and 0.205 MWh for conventional and proposed techniques respectively. In addition, three approaches are compared to assess generation adequacy corresponding to different schedules. The study emphasizes the efficacy of employing the LCM approach in scheduling generator maintenance. Specifically, it showcases that among all the methods examined, the LCM approach exhibits the lowest variance in objective function values, with values of 38.81 and 39.90 MWh for LCM and its closest rival, the modified particle swarm method (MPSM), respectively.

An optimization approach for the economic dispatch incorporating renewable energy resources into Lesotho power sources portfolio

Electricity demand in Lesotho has surpassed the main domestic generation of 72-MW hydropower station with 59% capacity deficit currently met by imports from South Africa and Mozambique through costly fixed bilateral contracts. With the abundant renewable energy sources in Lesotho, independent power producers could be incentivized to erect solar PV plants and wind farms to increase local energy security at lower cost and diversify utility's power mix. This article develops a power dispatching approach that prioritizes solar PV and wind generators to aid hydropower station meet demand and only be backed by imports. It uses the Monte Carlo approach to simulate generation adequacy analysis in order to establish monthly average expected demand not supplied (EDNS) and loss of load probability (LOLP) for the baseline case and three dispatch scenarios. According to the analysis, the EDNS for all simulated scenarios never drops to 0 MW, while the LOLP only reaches a minimum of 52% for the scenario with all local renewable energy generators combined. Furthermore, main grid energy imports can be minimized by 22.3% with the introduction of 50-MW solar PV and by 40.2% for 58-MW wind farms. A 59.7% minimization can be obtained by combining solar PV at 50-MW, wind farms at 58-MW and MHP at 72-MW. These introductions would lead to subsequent reductions in power procurement costs of about 6.2% for solar PV alone and 1.11% for both solar PV and wind. However, the inclusion of wind energy alone would lead to slight cost increase of about 0.6%.

Assessment of Generation Adequacy Taking into Account the Dependence of the European Power System on Natural Gas

Assessment of Generation Adequacy Taking into Account the Dependence of the European Power System on Natural Gas

Generating multivariate load states using a conditional variational autoencoder

For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed. Going beyond common CVAE implementations, the model includes stochastic variation of output samples under given latent vectors and co-optimizes the parameters for this output variability. It is shown that this improves statistical properties of the generated data. The quality of generated multivariate loads is evaluated using univariate and multivariate performance metrics. A generation adequacy case study on the European network is used to illustrate model's ability to generate realistic tail distributions. The experiments demonstrate that the proposed generator outperforms other data generating mechanisms.

Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040

Electric vehicles (EVs) are expected to grow massively in the coming years, and grid integration of a large number of them could challenge electricity-system infrastructure. This paper aims at describing a methodology to study the technical and economic impacts on power systems of mass EV charging for several EV-owner connection behavior profiles (systematic, when necessary, when convenient) and the range of recharge modes available (uncontrolled, time-of-use tariff, smart unidirectional charging, and vehicle-to-grid). This framework is applied to a case study at hourly resolution of high penetration of electric vehicles and renewable energy sources in Europe at the 2040 time-horizon, in line with the ‘National Trends Scenario’ grid mix under the pan-EU ENTSO-E Ten-Year Network Development Plan. Results show that the European electricity system can accommodate large EV growth and that widespread adoption of smart charging in France can significantly reduce operational electricity system costs by up to 1.1 G€ and reduce carbon emissions by up to 3.2 MtCO2 per year. Multiple EV smart charging modes are also compared and the parameters have the largest impact on EV flexibility are identified, including gas prices, smart charging adoption, weekly flexibility, and mid-day charging.

Application of Generation Adequacy Analysis for Reliability Evaluation of a Floating Production Storage and Offloading Platform Power System

This paper proposes the application of generation adequacy analysis for reliability evaluation of an insulated power generation system that supplies a FPSO (Floating Production Storage and Offloading) oil and gas production platform. The frequency and duration method was adopted for generating system reliability evaluation. The historical reliability data of the floating production storage and offloading platform power system and the continuous Markov process are used to determine the generator’s reliability model. The load model was also based on the platform daily peak load variation curve. The system risk indexes were obtained using Monte Carlo simulation. Two system scenarios were simulated using different failure data for one of the generators and the software PowerFactory© has been used as a tool for this simulation. For a complete generation system modeling, frequency and duration methods were developed to calculate the probabilities, frequencies and duration of the system states. Numerical results are presented and discussed to demonstrate the applicability of the proposed method.

Reliability standards and generation adequacy assessments for interconnected electricity systems

This paper studies the consistency between two contradictory policies in the electricity industry. On the one hand, electricity systems are increasingly interconnected. On the other hand, reliability standards, whose value was typically set when countries were hardly interconnected, are still enforced at the national level. We show that enforcing autarky reliability standards may still reach the welfare optimum in the presence of interconnections, but only under two conditions. First, installed generation capacities should be determined jointly, while considering the whole power system. Second, reliability calculations should fully internalize external adequacy benefits occurring in neighboring systems. We run a numerical application for a set of European countries and find that existing interconnections may lead to generation adequacy benefits of around one billion euros per year, by enabling a 18.9 GW decrease in generation capacity. In our case study, regional coordination is found to be more important than fully internalizing external reliability benefits in adequacy simulations.

Phasing out coal: An impact analysis comparing five large-scale electricity market models

Climate target achievement has a crucial influence on the modelling and the decision processes in the energy sector. It has motivated the development of several policy instruments to mitigate greenhouse gas emissions, including administrative and market-based mechanisms for phasing out coal-fired generation technologies. In order to analyse such instruments, electricity market and energy system models are widely used. However, the results and the corresponding recommendations largely depend on the formulation of the respective model. This motivates a systematic comparison of five large-scale electricity market models which are applied to European scenarios with a focus of the analysis on Germany considering the period until 2030. A method for the identification of model differences is proposed to analyse the effects of two coal phase-out strategies. This contribution expands on earlier studies and provides some more general takeaways for both modellers and decision-makers. For instance, our analysis demonstrates the influence of the reference scenario on evaluating a policy instrument. Furthermore, the importance of technical aspects, such as the constraints for combined heat and power plants, are discussed, and implications regarding three dimensions (the generation adequacy, economic efficiency and environmental sustainability of power supply) are presented.

Investment analysis of power producers under reliability option scheme

Reliability Options (ROs) are one type of Capacity Remuneration Mechanisms (CRMS) to meet generation adequacy requirement, and it has been applied in some power markets. When a power producer sells a RO, he will receive an option fee but have to sell power at a price below the strike price which is set by the buying side. This mechanism may have some impacts on the investment decisions of power producers. In this paper, the investment timing of power producers is studied. A stochastic differential electricity price model and an investment decision model are built. Some characteristics, such as mean-reversion, multi-periodic and gradual growth are considered in the price model. Real option pricing model is used to make generation investment decision and it is equivalent to a linear complementarity problem. The numerical solution is obtained by the Successive Over Relaxation (SOR) method. The example results show that the different settings of strike price and option fee will lead to early or late investment.

Stochastic AC Transmission Expansion Planning: A Chance Constrained Distributed Slack Bus Approach With Wind Uncertainty

Integrating more renewable energy sources into the grid leads to a more vulnerable power system and presents challenges for power system planners. This paper proposes a probabilistic overload constraint based AC transmission expansion planning model. In terms of economic dispatch, generation adequacy and network constraints are assessed using a probabilistic participation factor power flow. Generation participation factors, which are handled in the AC power flow equations, are used to manage mismatch power that comes from uncertainties as well as transmission loss of the system in a distributed slack bus concept. Combinations of load, wind and N-1 contingency uncertainties are evaluated with Monte Carlo Simulation and the loading limits for the existing and candidate lines and violations on bus voltages are enforced as probabilistic constraints using chance constraint programming. The investment decisions taken under various operating conditions are re-evaluated under a new set of wind power uncertainty that differs from those utilized in the optimization process, and the resulting TEP decisions are analyzed from a risk perspective. The proposed method allows for the uncertain operating conditions to be easily and accurately adapted to the ACTEP optimization. The optimization results show that the participation factor power flow is a promising tool for evaluating probabilistic operating conditions, which can be used to explore the possibility of violations under different uncertainty conditions.

Reliability Standards and Generation Adequacy Assessments for Interconnected Electricity Systems

Reliability Standards and Generation Adequacy Assessments for Interconnected Electricity Systems

Experiences and Lessons Learned From DR Resources Participating in the US and UK Capacity Markets: Mechanisms, Status, Dilemmas and Recommendations

The role of demand response resources is particularly important under the new-type power system with new energy as the main source. Demand response resources are important for generation adequacy and can provide electricity capacity value for power system. The United States and the UK are the only countries in the world that offer capacity services from demand response resources as generation resources. Their experiences and lessons learned are of great significance to other countries and regions. This paper first analyzes the significance of DR resource participating in the capacity market from two perspectives: the development of demand response resources and system generation capacity. Secondly, a detailed analysis of the mechanism design of demand response resources in the US capacity market is presented and the development status of demand response resources since 2007 is provided, so as to provide meaningful reference for other countries and regions. Thirdly, this study introduces the current situation and dilemma of demand response resources in the capacity market of the UK, and provides corresponding suggestions so that demand response resources in other countries and regions can avoid repeating the pace of the UK in the capacity market. Finally, suggestions for demand response resources to participate in the capacity market are given from four aspects.