Variable Renewable Generation Research Articles

Distributed Energy Resource Integration for Carbon Neutral Power Systems: Market‐Based Approaches to Ancillary Services and Microgrid Operation

Electricity, as a sustainable energy carrier, plays a central role in the transition scenarios for carbon neutralization of energy systems. Expanding the potential of electricity requires intelligent integration of electricity infrastructures and electricity markets with distributed energy resources (DERs) including roof‐top solar photovoltaics (PVs), controllable loads, and storage batteries. This integration will advance the management of power distribution grids, resulting in harmonization of the distribution grids and variable renewable generation. In the foreseeable future, the DERs will be actively used to perform advanced management of power distribution grids and compensate fluctuation in variable renewable power generation such as PVs and wind power. Market‐based coordination mechanisms are already practiced in large‐scale electricity production, electricity trading and transmission around the world. Using these mechanisms at the distribution grid level to integrate numerous flexible devices into the grids remains an important research topic. This paper reviews the role of DER integration toward carbon‐neutral power systems, the state‐of‐art and challenges associated with market‐based approaches for integrating flexible demand, distributed generation, and storage. Methods for assessing the value of DER aggregation such as virtual power plants and microgrids are also explored. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Joint contribution of RTEM and AGC system for frequency stabilisation in renewable energy integrated power system

AbstractIncreasing penetration of variable renewable generations will diminish system inertia thereby degrading the conventional frequency regulation capability. As a result, maintaining frequency stability will be more and more challenging with traditional approaches. Even though renewable sources integration would jeopardise the grid stability, it also presents several opportunities as well. For example, converter‐interfaced generators can bid in real‐time electricity markets (RTEM) and provide short‐time dispatch to minimise load‐generation mismatch. In this paper, an integrated approach that accommodates discrete automatic generation control (AGC) system with a regulation mileage framework and RTEM model to balance generation and consumption is proposed. The RTEM model is assumed to have a five‐minute dispatch trading interval which is to some extent comparable to the discrete AGC system. Furthermore, a fractional order PID (FOPID) controller is equipped in the AGC system whose parameters are tuned using a novel metaheuristic‐based optimisation called Lichtenberg Algorithm (LA). The proposed framework is tested in a three‐area system under several operating conditions to reveal the improvement in the dynamic performance of the system. The objective function is also incorporated with mileage payment that allows a fair compensation rule for all the units.

The potential of decentral heat pumps as flexibility option for decarbonised energy systems

Decarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO2 emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWhel and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation.

A nationwide multi-location multi-resource stochastic programming based energy planning framework

With the escalating global energy demands, nations face greater urgency in diversifying their electricity market portfolio with various energy sources. The efficient utilization of these fuel resources necessitates the integrated optimization of the entire energy portfolio. However, the inherent uncertainty of variable renewable energy generators renders traditional deterministic planning models ineffective. Conversely, expansive stochastic models that capture all the complexities of the power generation problem are often computationally intractable. This paper introduces a scalable energy planning framework tailored for policymakers, which is adaptable to diverse geopolitical contexts, including individual countries, regions, or coalitions formed through energy trade agreements. A two-stage stochastic programming approach, including a scenario-based Benders decomposition method, is employed to achieve computational efficiency. Scenarios for representing parameter uncertainty are developed using a k-means clustering algorithm. An illustrative case study is presented to show the practical application of the proposed framework and its ability to inform policymaking by identifying actionable cost-reduction strategies. The findings underscore the importance of promoting resource coordination and demonstrate the impact of increasing interchange and renewable energy capacity on overall gains. Notably, the study shows the superiority of stochastic optimization over deterministic models in energy planning.

Electricity Generation From Cow Manure Compared to Wind and Photovoltaic Electric Power Considering Load Uncertainty and Renewable Generation Variability

Electricity Generation From Cow Manure Compared to Wind and Photovoltaic Electric Power Considering Load Uncertainty and Renewable Generation Variability

Electricity sector impacts of water taxation for natural gas supply under high renewable generation

The future of energy development could be soon restrained by water scarcity. Shale gas, which is highly water-consuming in its extraction, is even more vulnerable. Thus, understanding the power system’s response to water constraints considering uncertainties from variable renewable energy generation becomes critical. This study presents a multi-stage stochastic Mixed Complementarity Problem for the energy sector, considering renewables variability and water scarcity’s impact on natural gas extraction. The power and natural gas sectors were modeled with hourly and daily resolution, respectively. Results demonstrate that stochastic planning enables smoother natural gas production and storage strategies, ensuring consistent operations with a narrower production range. Water taxation affects gas production and the generation mix, leading to electricity price changes. Gas-based electricity generation varies up to 16.2% based on water taxation and wind availability. Furthermore, water taxation of up to 20% can raise gas prices by up to 21.1%. This study underscores the need to integrate water scarcity considerations into energy planning, offering insights into sustainable decision-making, water resource management, reliable electricity generation, and price stability amidst evolving renewable portfolios.

Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management

The integration of fluctuating renewable resources such as wind and solar into existing power systems poses challenges to grid reliability and the seamless incorporation of these resources. To address the inherent variability in renewable generation, direct load control emerges as a promising method for demand-side management. Thermostatically controlled appliances, like air conditioners, hold a significant role in this approach. However, effective direct load control necessitates accurate load magnitude estimation and the potential for load shifting. In this paper, we introduce a smart-agent architecture that employs a mathematical model to forecast aggregated power consumption behavior, even when changes are introduced by the controller. To assess system performance, a numerical simulator was developed, demonstrating the system’s adaptability to changes, its self-retraining capability, and its continuous improvement in predicting aggregated power consumption.

Transformation to a renewable electricity system in Austria: Insights from an integrated model analysis

We analyse the (techno- and macro-)economic and distributive effects of a transformation to a renewable electricity system in Austria by 2030, as stipulated by the Austrian government. For the analysis, the macroeconomic model DYNK and ATLANTIS, a partial model of the electricity market, were expanded and linked. Four transformation scenarios conforming with the 100% renewable electricity target in Austria on a national balance are examined, integrated into a consistent scenario for the development of the European electricity system. Additionally, sensitivity analyses with respect to the gas price are performed. Although all scenarios achieve 100% renewable electricity on a national balance, the analysis shows that electricity from gas-fired power plants will still be needed in 2030 to balance variable renewable generation, to avoid grid congestion, and for heat generation from combined heat and power plants in winter months. Another main conclusion from the simulations is that the transition towards a renewable electricity sector is almost neutral from a socio-economic perspective. It does neither reveal harmful impacts nor lead to high multiplier effects from additional investment. With high natural gas prices in the sensitivity scenarios a decrease in GDP and household income, which might motivate redistributive policies, can be observed.

Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets

As the proportion of variable renewable generation capacity in our electricity systems rises, so too will the associated absolute forecast errors in their generation. This work quantifies the complex interactions between short-term renewable generation forecasts, short-term demand forecasts, generator unit commitment and economic dispatch in electricity systems with levels of variable renewable generation spanning 49–79% of annual electricity demand. When the short-term operational uncertainty caused by renewable generation becomes larger in absolute terms than that of demand, the accuracy of renewable generation forecasting has a critical influence on electricity market performance. That is, inaccurate short-term wind forecasts can increase hours of unserved energy and scarcity hours spent at market price cap, increase total system costs and prices, significantly increase returns to thermal generators and energy storage, reduce returns to wind generators, and increase greenhouse gas emissions slightly. For the generalised case study considered in this work, the value of improved short term wind generation forecasts looks to becomes significant once variable renewables generate more than roughly 60% of annual electricity demand. Reductions in average market prices of 5 $/MWh or more then appear plausible as acceptable levels of reliability are recovered. As such, improvements in short-term renewable generation forecasting are likely required in order to justify continued investment in variable renewables as the major means of achieving decarbonisation efficiently and reliably, and these could be achieved by any combination of improved forecasting, dispatch control or on-site firming.

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to obtain the optimal solution with the lowest total cost including normal operation cost and battery degradation cost.

Energy Management of a Battery Storage System Considering Variable Load and Controllable Renewable Generation (Solar Study Case) to Keep the Grid’s Frequency Stability

This paper presents the research of analytical functions related to the energy generation of photovoltaic systems and the residential and commercial load demanded by end users, concerning a statistical function. To test this model, a linear cost function was considered to compute its overestimation and underestimation due to its maximum and minimum production limits, where energy consumption is obtained at each instant of time, within the established production ranges, through the analytical equations that determine solar energy generation and demand load. The result obtained by applying the Uncertainty Quantification (UCF) theory in these equations, in the same way through the Monte Carlo (MC) simulation for comparison, is the expected value of energy for a hypothetical storage system E (Cu, Co). Better accuracy of results via this model can be improved upon when the energy generation parameters are structured as analytical functions each instant of time associated with probability distributions based on the uncertainty costs of controllable sources, instead of statistical functions.

Flexibility extension in hydropower for the provision of frequency control services within the European energy transition

This paper presents a comprehensive assessment of the large-scale deployment of hydropower on the provision of frequency regulation services, when equipped with the extended flexibility solutions being developed and/or tested within the scope of the XFLEX HYDRO project. The current analysis is performed on the Iberian Peninsula (IP) power grid considering its interconnection to the Continental Europe (CE) system, since this power system zone is expected to have the most severe frequency transient behaviour in future scenarios with increased shares of variable renewable energies. For this purpose, prospective scenarios with increased shares of time variable renewable generation were identified and analysed. To assess the impacts of the hydropower flexibility solutions on frequency dynamics after a major active power loss, extensive time domain simulations were performed of the power system, including reliable reduced order dynamic models for the hydropower flexibility solutions under evaluation. This research assesses the effects of synchronous and synthetic inertia, and of the Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) services as specified in European grid codes. The main findings highlight the potential of hydropower inertia and of adopting a variable speed technology for enhancing frequency stability, while contribute to better understand the role of hydropower plants in future power systems.

The mid-transition in the electricity sector: impacts of growing wind and solar electricity on generation costs and natural gas generation in Alberta

In response to climate change, electricity grids are decreasing their carbon intensity with the addition of wind and solar variable renewable energy generation (VREN). This leads to a mid-transition period, where renewable energy is unable to satisfy electricity demand without contributions from other fossil sources such as natural gas, but also generates sufficiently to constrain conventional generation—changing their operating and market conditions. We use a simplified copper plate model, which scales up and down historical wind and solar generation, to examine how and when the patterns and generation costs for fossil fuel power could change by the increasing capacities of VREN on the relatively isolated Alberta electricity grid. We find that beginning at 20% VREN an increasingly diverse range and reduced hours of dispatched capacity is necessitated from the existing generation. However, even as capacity factors for fossil fuel generation decrease their costs remain reasonable and we found this to be a low-cost pathway for achieving moderate to deep emission reduction goals. A full 86% of demand could be met with VREN before generation costs exceeded 100$/MWh, allowing for an emissions reduction of 28.4–9 million tonnes yr−1 of CO2eq, on a lifecycle basis. In order to integrate the renewable generation, new and existing fossil fuel units will require market rules that incentivise flexibility and ensure they remain in place throughout the transitionary period as they are crucial to balance variable renewable generation.

Probabilistic spatiotemporal scenario generation method for dynamic optimal power flow in distribution networks

Due to the intricate intertwining of spatiotemporal characteristics and the substantial computational complexity caused by high dimensionality, the simultaneous consideration of both spatial and temporal correlations is a non-trivial problem in power system scheduling with variable renewable energy sources. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method for the dynamic optimal power flow problem in distribution networks, which generates probabilistic scenarios considering the spatial and temporal correlations among random variables simultaneously. First, static scenarios are generated by Latin hypercube sampling, and the probability of each static scenario is determined by copula-importance sampling theory, which is proposed to ensure the static scenarios yield the same probabilistic characteristics as the random variables. Next, a probability-based scenario reduction technique is implemented to retain effective scenarios and lower the computational burden. Then, multiple linear regression generalizes the static scenarios into dynamic ones with the same probabilities. Finally, a spatiotemporal scenario-based stochastic optimization is proposed for the dynamic optimal power flow problem, which considers uncertainties in variable renewable energy generation and aims to balance the output of controllable generators, renewable energy curtailment, and load shedding. Numerical simulations in a modified IEEE 33-node distribution network show that the proposed approach outperforms the existing methods that do not simultaneously consider spatial and temporal correlations in terms of computational efficiency and accuracy, proving that the proposed PSTSG method can efficiently capture the spatial and temporal correlation so as to make the optimal scheduling decision.

A Lagrangian relaxation approach to an electricity system investment model with a high temporal resolution

AbstractThe global production of electricity contributes significantly to the release of carbon dioxide emissions. Therefore, a transformation of the electricity system is of vital importance in order to restrict global warming. This paper proposes a modelling methodology for electricity systems with a large share of variable renewable electricity generation, such as wind and solar power. The model developed addresses the capacity expansion problem, i.e. identifying optimal long-term investments in the electricity system. Optimal investments are defined by minimum investment and production costs under electricity production constraints—having different spatial resolutions and technical detail—while meeting the electricity demand. Our model is able to capture a range of strategies to manage variations and to facilitate the integration of variable renewable electricity; it is very large due to the high temporal resolution required to capture the variations in wind and solar power production and the chronological time representation needed to model energy storage. Moreover, the model can be further extended—making it even larger—to capture a large geographical scope, accounting for the trade of electricity between regions with different conditions for wind and solar power. Models of this nature thus typically need to be solved using some decomposition method to reduce solution times. In this paper, we develop a decomposition method using so-called variable splitting and Lagrangian relaxation; the dual problem is solved by a deflected subgradient algorithm. Our decomposition regards the temporal resolution by defining 2-week periods throughout the year and relaxing the overlapping constraints. The method is tested and evaluated on some real-world cases containing regions with different energy mixes and conditions for wind power. Numerical results show shorter computation times as compared with the non-decomposed model and capacity investment options similar to the optimal solution provided by the latter model.

Bi-level goal programming model for simultaneous microgrid investment and tariff design: A case study in the Volta Region of Ghana

The use of microgrids for remote rural electrification in developing countries requires solutions to technical and financial constraints involving variability in renewable generation and electricity demand, budget limitations, and underlying electricity tariffs. While previous research has evaluated a subset of these problems, to the best of the authors’ knowledge, no attempt has been made to design an optimal tariff structure that promotes an isolated microgrid’s business case for rural electrification. This work presents a new goal-oriented bilevel optimization model capable of simultaneously optimizing the microgrids tariff design–to offer full revenue recovery for the investor while also ensuring equitable electricity prices for low-income households–as well as optimizing the physical microgrid design. A case study of an off-grid island on Ghana’s Volta Lake demonstrates the efficiency of the new tariff design over the existing policy. For a remote rural village, a 6kW solar PV, 5kW wind and 11kW diesel microgrid provide the highest lifecycle electricity supply adequacy at the lowest investment cost. Through scenario analysis, we demonstrate the implementation of an optimal tariff design capable of offsetting a 39 % revenue shortfall compared to an existing tariff structure while ensuring equitable electricity prices for low-income households.

Techno-economic analysis of a combined power plant CO2 capture and direct air capture concept for flexible power plant operation

The electric power sector must be deeply decarbonized at a reasonable cost to achieve economy-wide decarbonization goals via sustainable electrification of other sectors. While variable renewable energy generation is expected to dominate grid decarbonization efforts, variability creates the need for supply- and demand-side technologies that can operate flexibly to balance the system. This paper proposes a new concept for power plant flue gas capture integrated with a novel lime-based direct air capture technology to enable flexible operation of the power plant and achieve negative emissions under all operating scenarios. Steady-state models are developed, and sensitivity analyses are conducted on key process variables to investigate system performance. The results, presented at three different power plant loading levels (100 %, 40 %, and 0 %), demonstrate negative emissions of -0.188 tCO2/MWh at 100 % loading and -2.087 tCO2/MWh at 40 % loading. Economic modeling suggests the need for carbon credits exceeding $170/tCO2 to achieve positive net present values.

Optimal Grid Flexibility Assessment for Integration of Variable Renewable-Based Electricity Generation

This study delves into power system flexibility, with a keen focus on the integration of variable renewable electricity generation into power grids. Two scenarios were analyzed. The base scenario revealed an aging grid, insufficient generation capacity, frequent outages, and little renewable energy generation (1.9%), along with a significant (71.23%) loss of load. In contrast, the investment scenario presented solutions including raising VRE capacity to 44%, adding 1000 MW capacity transmission lines, installing 200 MW capacity grid-scale battery storage, and technological enhancements. These interventions effectively eliminated loss of load, reinforcing energy resilience. Investments in CCGPP and grid-scale batteries proved instrumental in mitigating the variability of renewable energy. Improved transmission promised efficient power exchange and regional collaboration. The elimination of annualized energy spills and the removal of ramping constraints marked significant strides in enhancing power system flexibility. This research underscores the pivotal role of grid flexibility in accommodating VRE sources. By implementing the proposed optimal solutions, Afghanistan can lead the way toward a cleaner, more resilient, and more interconnected energy future. These findings offer a replicable framework for addressing similar challenges in integrating renewable energy sources globally and supporting the transition to sustainable and reliable energy.

Is Power-to-Gas always beneficial? The implications of ownership structure

Power-to-gas (PtG), a technology that converts electricity into hydrogen, is expected to become a core component of future low-carbon energy systems. While its economics and performance as a sector coupling technique have been well studied in the context of perfectly competitive energy markets, the distortions caused by the presence of large strategic players with a multi-market presence have received little attention. In this paper, we examine them by specifying a partial equilibrium model that provides a stylized representation of the interactions among the natural gas, electricity, and hydrogen markets. We calibrate our model using the future Dutch energy system as a reference. Using this model, we compare several possible ownership organizations for PtG to investigate how imperfect competition affects its operations. Evidence gained from these market simulations show that the effects of PtG vary with the multi-market profile of its operator. Producers of fossil-based hydrogen tend to make little use of PtG, whereas renewable power producers use it more to increase the electricity prices. Although PtG operations are profitable and can be welfare-enhancing, welfare distribution among agents is highly unequal when PtG is strategically operated in conjunction with variable renewable generation. In that case, PtG also raises environmental concerns as it stimulates the use of polluting thermoelectric generation.

A Risk-Based Planning Approach for Sustainable Distribution Systems Considering EV Charging Stations and Carbon Taxes

Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewable production, and energy prices. Therefore, this work proposes a novel model for the multi-period planning of EDSs and DERs considering conditional value at risk (CVaR) to manage fluctuations in generation cost and carbon emissions. The proposed mathematical model aims to minimize the net present cost related to investment, operation, and risk. Unlike previous approaches, uncertain behavior of demand growth per planning period is addressed, and the risk is evaluated from two perspectives: planning costs and carbon taxes. Investments in substations, lines, renewable distributed generation, EV charging stations, and energy storage systems are considered. The uncertainties associated with the variability of renewable generation and demand are modeled through a set of scenarios. Finally, the model was evaluated using the 24 and 54-bus EDS. Thus, the proposal is a flexible tool that can be used for different purposes (e.g., carbon taxes, budget limits).