Variable Renewable Energy Generation Research Articles

Decarbonizing the power system by co-planning coal-fired power plant transformation and energy storage

The integration of variable renewable energy (VRE) and the gradual phase-out or functional transformation to coal-fired power plants (CFPP) are two essential transition pathways in achieving the carbon neutrality in China. However, the withdrawal of a large amount of CFPPs may lead to the reduction of inertia and the deficiency of flexibility in power systems, as a flexible alternative, energy storage is playing an increasingly important role in leveling fluctuant VRE output. Therefore, this paper proposes a co-planning approach to the CFPP transformation and battery energy storage system (BESS) accompanying with VRE integration. Two options are considered in the transformation path of CFPPs: retirement or reconstruction. Carnot battery, a novel thermal storage reconstruction scheme, is proposed for CFPP reconstruction, which is then comprehensively compared with BESS in terms of technical, economy and CO2 emission reduction. To ensure the frequency stability in the power system low-carbon transition, the frequency security constraints considering the fast frequency response provided by BESS is embedded in the co-planning model. In addition, the uncertainty of VRE is considered through a robust optimization method. To efficiently solve the problem, the nested column-and-constraint generation (C&CG) algorithm is employed. The case studies are implemented on the IEEE test system and the Shanxi Power System in China. The results show that the co-planning of CB reconstruction to CFPPs and BESS can promote carbon emission reduction and VRE generation accommodation, ensure operation security of decarbonized power systems. Some generation transition paths for decarbonizing the Chinese Power System are suggested in the end.

Flexible electric vehicle charging and its role in variable renewable energy integration

Uptake of electric vehicles is accelerating as governments around the world aim to decarbonize transportation. However, swift and widespread electric vehicle (EV) adoption will require some degree of controlled charging to mitigate the adverse impacts of electric vehicle adoption. Simulating the interaction between transportation and power requires new modelling tools with operational detail and spatial-temporal granularity. This analysis evaluates the potential benefits of utility-controlled charging (UCC) with the objective of reducing variable renewable energy (VRE) curtailment in decarbonized power systems using a framework that links travel and power system models using an intermediate charging model. Results show that the addition of VRE generation infrastructure shows the most impact on electricity system operating emissions and costs, but EV charging plays a significant role as well. Within EV charging strategies, UCC charging decreases emissions by 7% compared to uncontrolled charging. UCC is proven to be most effective in the summer due to higher electric vehicle fuel economy. Finally, the type of VRE generation infrastructure on the grid may have implications for siting of EV charging infrastructure due to the typical temporal peaks of wind and solar energy. These findings demonstrate how the use of distinct but linked travel and power sector models can be deployed to reduce multi-sector emissions and costs.

Synergetic Scheduling Energy and Reserve of Wind Farms for Power Systems with High-Share Wind Power

With wind and other variable renewable energy (VRE) generation gradually becoming the main power source, it is difficult to ensure the flexible and secure operation of the system by completely relying on conventional sources to balance the uncertainty and volatility of VRE. The increase of VRE penetration leads to the shortage of operation reserves, and wind power is allowed to participate in reserve services. However, the output of wind power is uncertain, and how to ensure the reliability and sustainability of wind power reserves is still an open question. A robust energy and reserve dispatch method considering the flexibility of wind power is proposed to solve this problem. To effectively measure the output and reserve margin of wind power under uncertain scenarios, (1) the grid connection coefficient of wind turbine generators (WTGs) is defined, and the uncertainty set considering active wind power curtailment is established; and (2) based on two kinds of WTG control methods, the reserve model of wind farms is established. The model links the reserve capacity between the base scenario and uncertain scenarios through the reserve coefficient, which ensures that the wind power reserve determined in the base scenario is still deliverable in any uncertain scenario. The robust scheduling model determines the optimal dispatching and reserve plan of conventional units and wind farms by balancing the system operation economy and the schedulable reserve of wind power. The column-and-constraint generation algorithm is used to solve the model. The effectiveness of the proposed model is verified by the illustrative results of the IEEE normal system.

Distribution locational pricing mechanisms for flexible interconnected distribution system with variable renewable energy generation

Nowadays, distribution locational pricing mechanism has been widely used in the radial distribution network to guide the energy consumption and promote the renewables utilization. Aiming to explore the reasonable pricing policy which is applicable to the merging flexible interconnected distribution system (FIDS), this paper develops two different distribution locational pricing mechanisms for distribution lines interconnected by soft open points (SOPs). First, based on the Wasserstein ambiguity sets for uncertainties from the main grid and distribution network, a carbon-oriented distributionally robust economic dispatch model is constructed for FIDS considering different price and carbon signals from the main grid. Then, by leveraging the optimal scheme from economic scheduling model, distribution locational marginal prices (DLMPs) and equivalent DLMPs (EDLMPs) are presented to measure the marginal cost and actual cost for energy consumption in the FIDS respectively. The calculation and decomposition methods of these price signals are also developed. In case studies, a practical 63-node FIDS is chosen to verify the effectiveness of the proposed methods. Numerical results illustrate the characteristics of DLMPs and EDLMPs in FIDS under multiple uncertainties. Comparing to DLMPs, EDLMPs are more appropriate to FIDS because this pricing mechanism can reflect the lower generation cost of renewables for each consumer.

Resistance is futile - implementing automated renewable trading in competitive energy markets

The recent introduction of five-minute settlements into the Australian National Electricity Market (NEM) in October 2021 saw a massive increase in the number of offers submitted to the market at every five-minute Dispatch Interval. While many of these offers were from non-renewable generators, a significant proportion was now from Variable Renewable Energy (VRE) Generators that had rarely submitted offers to the market before the market change. VRE generators use various approaches to actively manage their market risks, including internal and external multi-generator operations centres, advanced Machine Learning optimisers and simple single generator revenue optimisers. This paper will discuss the advantages and risks of all three approaches, how each has operated in the market, and how strategies adapt to the dynamic competitive energy and ancillary markets. Surprisingly, the simple and usually lower-cost option of a simple revenue optimisation seems to provide a low-risk, reliable and robust alternative for VRE generators and can also be combined with other systems to meet VRE compliance and other operational priorities generators. More sophisticated systems are required for multi-modal and Battery Energy Storage Systems (BESS) as the forecast horizon needs to consider an entire trading day operations due to the physical constraints of the Lithium-Ion technology. However, the same revenue optimisation approach can work for other battery technologies, such as supercapacitors, that do not have the cycle constraints of Lithium-Ion and are especially suited to providing frequency and contingency ancillary services.

Effects of meteorological and climatological factors on extremely high residual load and possible future changes

Under high variable renewable energy (VRE) penetration, the occurrence of low VRE production, such as dark doldrums (Dunkelflaute) and wind-solar drought, can threaten a secure and continuous energy supply owing to an imbalance between electricity demand and supply. In this study, historically reconstructed long-term VRE generation and electricity demand were used to investigate the relationship between extremely high residual load (demand minus VRE output) and weather/climate in Japan. The impact of changes in the VRE's installed capacity on this relationship was also investigated using three simple future target scenarios. The results showed that the increase in installed VRE capacity causes greater daily and weekly residual load variabilities and affects the seasonality of its peaks. To study the weather patterns associated with high residual load events, self-organizing maps were applied to atmospheric circulation fields derived from atmospheric reanalysis data. The high residual load was associated with enhanced cold surge-type weather patterns during winter at the current low VRE installation level. However, under future increased VRE penetration, the weather patterns leading to high residual load will change to cloudy-windless types typically caused by a southern coastal extratropical cyclone. There is also considerable interannual variability in the frequency of high residual load events, which changed significantly with increasing VRE capacity. It is crucial to incorporate the dependence of climatic conditions into designing power systems to maintain the stability of a power system under future conditions.

A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence

The generation from renewable sources has increased significantly worldwide, mainly driven by the need to reduce the global emissions of greenhouse gases, decelerate climate changes, and meet the environmental, social, and governance agenda (ESG). The main characteristics of variable renewable energy (VRE) are the stochastic nature, its seasonal aspects, spatial and time correlations, and the high variability in a short period, increasing the complexity of modeling, planning, operating, and the commercial aspects of the power systems. The research on the complementarity and dependence aspects of VREs is gaining importance, given the development of hybrid generation systems and an array of VREs generators spread over a large region, which could be compounded by different renewable sources, such as hydro, solar, and wind. This review is based on a systematic literature review, providing a comprehensive overview of studies that investigated applied methodologies and methods to address dependence and complementarity. It is a recent field of interest, as 60% of the articles were published in the last five years, a set of methods that have been employed to address this issue, from conventional statistics methods to artificial intelligence. The copulas technique appears as an important approach to modeling renewable energy interdependence. There is a gap in articles comparing the accuracy of the methods employed and the computational efforts.

Towards reliability competition: Non-cooperative market mechanism under high variable renewable energy penetration

To encourage variable renewable energy (VRE) penetration, scholars put efforts in building cooperative market mechanisms. Under these mechanisms, VRE generators are treated as negative uncontrollable loads. The advantage of cooperative mechanism is that by cooperative aggregation of the uncertain sources (VREs and uncontrollable loads), some fluctuations cancel out. However, this study points out that the nature of cooperative mechanism will hinder further improvement for either the society-wise production efficiency or the VRE-wise economic efficiency. The key issue is that it is hard for the cooperative mechanisms to define positive marginal cost for VRE, thus blocking the value chain of carbon reduction. To solve the problem, this study proposes to consider reliability as a production characteristic of VRE generators. Based on these proposals, this study establishes a novel non-cooperative reliability competition mechanism for VRE market participation. Under this mechanism, the system is able to generated power at the lowest marginal cost considering both carbon cost and reliability cost. What’s more, the numerical simulation shows that: more VRE generators are able to achieve higher economic efficiency under non-cooperative mechanism compared to cooperative mechanism; the non-cooperative mechanism can provide clear signals for the internal and external marginal cost of generation reliability of VRE. It is shown that under the proposed mechanism, the reliability of VRE-included power generation system can reach the limit of technology in the long run.

Exploiting the operational flexibility of AA-CAES in energy and reserve optimization scheduling by a linear reserve model

Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) is expected to play a crucial role in providing energy shifting and fast regulation to variable renewable energy generation because of its large capacity and fast response. However, due to the complex operation characteristics of AA-CAES, there are few studies on AA-CAES optimal operation providing energy and reserve for power systems. Therefore, a new robust scheduling approach is proposed, in which the operation of AA-CAES is precisely characterized by the synergetic energy and reserve market. To fully exploit the operational flexibility of AA-CAES, this paper proposes a linear reserve model of AA-CAES by quickly switching working modes. The energy storage-reserve constraint is established to ensure the deliverability of reserve services. Then, an enhanced column and constraint generation (C&CG) algorithm is proposed to solve the proposed model. Finally, the proposed method is applied to the Garver-6 bus and IEEE-118 bus systems in the MATLAB operating environment. Numerical simulation results indicate that AA-CAES participating in power system scheduling can reduce the generation and reserve cost of conventional units, and improve wind power accommodation. By quickly switching the working mode, the adjustable reserve margin of AA-CAES can be effectively improved. In addition, compared with the existing nested C&CG algorithm, the enhanced C&CG algorithm effectively improves the solution efficiency of the proposed model.

Profitability and the use of flexible CO2 capture and storage (CCS) in the transition to decarbonized electricity systems

• Fossil power with CCS will need to operate flexibly to ensure grid stability. • The profitability potential of flexible CCS is assessed in four national markets. • We show how a fraction of power plants can be profitable in each market. • This suggests a possible role for existing fossil plants in grid decarbonization. As the share of variable renewable energy (VRE) generation in a system increases, services such dispatchable “firm power” will be vital to ensure system reliability. Fossil power plants with CO 2 capture and storage (CCS) can be a source of firm power, but they must be operated in a flexible manner to support grid stability. This paper considers the use of CCS to pivot the role of at least some existing fossil plants towards providing firm power in decarbonized grids. We present an analysis of the profitability potential of CCS-equipped power plants in electricity markets in US, UK, Australia and China. Conventional wisdom holds that CCS must be operated in a baseload manner to be economically viable; we demonstrate that the flexibly-operated power plants with CCS are not generally profitable today, but can become profitable in cases with a proper combination of market incentives and cost reductions in capture technology; we highlight the possibility of retrofitting older plants with mostly depreciated capital for operation a “second life” peaker mode to retain investment flexibility during an energy system transition as an intriguing option requiring further study. Flexible operation offers less value in China's electricity markets due to more rigid pricing structures, but a plausible path towards profitable CCS-firm power exists for lower cost power plants in some regions of the country. The results presented here imply a strong potential for the profitable operation of CCS retrofitted to amortized assets, acting in a market-compatible way in synergy with greater integration of renewable energy to the power grid, whilst contributing to overall grid stability. As price volatility is likely to increase with increased penetration of VRE, the trend of these results are likely to become more pronounced with the passage of time.

An Improvement of Stability in Distribution Network by Integrated Photovoltaic-Electric Spring System

The electricity grid's distribution system is a crucial component. Power quality issues in the distribution system have received a lot of attention in recent years due to the extensive integration of variable renewable energy generation. Modern digital grid technology, electric spring (ES), has previously been utilised to stabilise voltage and power in a system that relies heavily on unregulated or completely renewable energy sources. To control voltage and power, it has been recommended as a demand-side strategic approach. It has been suggested that electric springs (ES) might be used as a request technology to boost the reliability and efficiency of future power grids that rely heavily on intermittent renewable energies. At now, ES is mostly used for controlling grid voltage and service frequency. This study describes a method for controlling and balancing power in a newly integrated ES and PV system configuration, and it examines how this method may be used to achieve responsive supply equilibrium in electricity distribution networks. The suggested system allows for the maximum amount of collected PV power to be sent to the grid via the ES, while also actively regulating the energy usage of its ES-associated smart load in order to maintain a constant supply-demand power balance. Because the smart-load power associated with the ES follows a suitable usage patterns to adjust for possible forecast failures of the PV power output, battery storage is unnecessary in the suggested architecture. Excellent performance of the suggested design method is demonstrated using Matlab/Simulink simulations.

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

Achieving the European Union renewable energy source penetration 2030 targets requires an increase in flexible resources to compensate for the variability/intermittency of solar and wind generation to ensure system safety and balancing. Herein, two readily deployable flexibility solutions to balance demand/supply as an alternative to building additional thermoelectric reserves are proposed. How the transmission system OPERATOR can use the ancillary services provided by a flexible photovoltaic (PV) fleet (solar regulation) or PV/wind fleet (variable renewable energy (VRE) regulation) together with a suitable underforecast and proactive curtailment of variable renewable generation (aka, implicit storage) to reduce current and future Italian imbalances is shown. How these flexibility solutions can become even more effective when combined with a strengthening of the transmission grid is shown. The imbalance reduction achievable by 2030 through solar/VRE regulation strategies would be of the order of 20–50% with zonal balancing and 27–80% with nationwide balancing is found. Imbalance costs would remain comparable with the business‐as‐usual (thermal generation) costs. A proactive curtailment of 5–17% of the total VRE generation is the environmental cost of stabilizing the system using VRE plants, avoiding the construction of thermoelectric reserves.

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. We integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

How do variable renewable energy technologies affect firm-level day-ahead output decisions: Evidence from the Turkish wholesale electricity market

This paper presents firm-level estimates of how an increase in variable renewable energy (VRE) generation affects individual firms’ day-ahead available capacity and generation plans in a developing country, Turkey. In addition to having a fast growing energy demand, Turkey represents an understudied market case where the market leader, unlike the rest of the generators, is a public enterprise that potentially prioritizes political motives over economic efficiency. We first cluster firms in the Turkish day-ahead market based on their generation profiles using unsupervised machine learning and hourly data from years 2017 to 2019. Then, we estimate the short run marginal effects of the projected VRE generation on firms’ day-ahead available capacity and generation plans via panel data methods. Our results support the existing concerns that the public enterprise, especially during the times of financial turmoil, acts with the motivation to stabilize market prices. Additionally, we find that there is a substantial degree of heterogeneity in firms’ responses to VREs and individual responses significantly differ from the aggregate market response.

Optimal Sizing of Grid-Scaled Battery with Consideration of Battery Installation and System Power-Generation Costs

Variable renewable energy (VRE) generation changes the shape of residual demand curves, contributing to the high operating costs of conventional generators. Moreover, the variable characteristics of VRE cause a mismatch between electricity demand and power generation, resulting in a greater expected energy not supplied (EENS) value. EENS involves an expected outage cost, which is one of the important components of power-generation costs. A utility-scale battery energy storage system (BESS) is popularly used to provide ancillary services to mitigate the VRE impact. The general BESS ancillary-service applications are as a spinning reserve, for regulation, and for ramping. A method to determine optimal sizing and the optimal daily-operation schedule of a grid-scale BESS (to compensate for the negative impacts of VRE in terms of operating costs, power-generation-reliability constraints, avoided expected-outage costs, and the installation cost of the BESS) is proposed in this paper. Moreover, the optimal BESS application at a specific time during the day can be selected. The method is based on a multiple-BESS-applications unit-commitment problem (MB-UC), which is solved by mixed-integer programming (MIP). The results show a different period for a BESS to operate at its best value in each application, and more benefits are found when operating the BESS in multiple applications.

A State-of-Charge-Based Flexible Synthetic Inertial Control Strategy of Battery Energy Storage Systems

Power systems would face issues in system frequency stability when high scales of variable renewable energy generation are integrated in them. Battery energy storage systems (BESSs) with advanced control capability and rapid control response have become a countermeasure to solve the issues in system frequency stability. This research addresses a flexible synthetic inertial control strategy of the BESS to enhance the dynamic system frequency indices including the frequency nadir, settling frequency, and rate of change of the system frequency. To this end, the control loops based on the frequency excursion and rate of change of the system frequency are implemented into the d-axis controller of the BESS. The adaptive control coefficient of both control loops could be adjusted according to the instantaneous state of charge (SOC) so that it can inject more power to the grid at a higher SOC. The benefits of the proposed combined inertial control strategy are investigated with various sizes of disturbance and SOCs of the BESSs. Results successfully illustrate that the proposed combined inertial control strategy of the BESS is capable of enhancing the system frequency stability so as to promote variable renewable energy accommodation.

A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.

Metrics to describe changes in the power system need for demand response resources

Grid decarbonization efforts can benefit significantly from demand response (DR) resources. However, system-level changes that affect the net-load such as increased variable renewable energy (VRE) generation and widespread deployment of energy efficiency (EE) also affect the type, magnitude and timing of DR required to support the grid. In this study, we use publicly available system-level data to define seven metrics to assess how these changes affect system-level shed and shift DR needs. Specifically, there are four metrics for grid conditions when DR has the highest system value and three metrics for DR program design that were developed by considering the magnitude and temporal distribution of net-load. We also develop three stylized load shape profiles illustrating EE measure impacts and one high VRE generation profile to demonstrate the application of these metrics. The results confirm the robustness of the metrics to identify complex interactions between demand-side and supply-side resources that can affect the DR need. Widespread application of our metrics can help system planners and operators be cognizant of such interactions and identify the DR need for the system in a way that can be most valuable.

A stochastic techno-economic comparison of generation-integrated long duration flywheel, lithium-ion battery, and lead-acid battery energy storage technologies for isolated microgrid applications

Different energy storage technologies can be potentially integrated into microgrids to support variable renewable energy generators. Long-duration flywheel energy storage is considered a new contender in the energy storage market. This energy storage technology has been previously evaluated in a techno-economic study, but it did not consider uncertainties in the model input data. In this work, stochastic techno-economic comparison is performed using microgrid modeling and Monte-Carlo methods to compare long-duration flywheels, lithium-ion batteries, and lead-acid batteries for isolated microgrid and industrial facility. Results generally show a relatively high probability for long-duration flywheels to yield a lower leveized cost of storage (LCOS) and levelized cost of electricity (LCOE) compared to lithium-ion batteries in 2020. Higher probability can be attained when the overnight component cost of long-duration flywheels is reduced or when overnight diesel prices are high. However, the chances of long-duration flywheels yielding lower LCOS and LCOE rapidly decreases as time progresses. Long-duration flywheel manufacturers and promoters must find ways to accelerate price reduction by 2.5× from baseline to secure a better chance of yielding a lower LCOS in 2050.

Modeling the Energy Cooperation between Regionally Interconnected Aggregators using Bilateral Agreements

Traditional centralized power dispatching systems have inherent limitations in dealing with the increasing penetration of variable renewable energy generation in distribution networks. This study presents a flexible energy exchange mechanism for regionally interconnected aggregators in regards to VRE penetration. Direct energy exchange between neighboring aggregators is proposed as a promising method of maximizing social welfare (i.e. maximization of end-users benefits and minimization of energy costs). The main idea of the proposed mechanism is to develop a parallel energy exchange cooperation between physically connected aggregators ensuring operational efficiency of the power systems at a local level. Given that neighboring aggregators may have differences in their energy generation and usage patterns these differences can be utilized to bridge the mismatch of each other’s supply shortage. An operational scenario of regionally interconnected aggregators is presented and investigated. The initial simulation results have illustrated the effectiveness and reliability of the proposed model and method.