Variable Energy Resources Research Articles

Renewable energy contingencies in power systems: Concept and case study

This paper introduces the concept of renewable energy contingencies that represent long-term/extended variability of variable renewable energy (VRE) resources, namely, significant periods (e.g., days/weeks) of low wind/solar availability. These contingencies have not received much attention to date but are likely to emerge as a major issue in some countries such as India as the share of VRE increases. Using 38 years of climate model reanalysis data for wind over India, we demonstrate that low periods of wind contingency below long-term (Indian) national average of 5 m/s can extend for more than 100 days in several zones some of which are deploying large wind farms. Even in some of the best wind resource areas in India with long term average wind speed close to 7 m/s, low wind days (e.g., 5 m/s which is substantially below average) can extend up to 60 days. We propose a four-step methodology around a co-optimization based energy-ancillary services dispatch model to assess the impact of renewable contingency and implemented it for the state of Tamil Nadu, the most wind-rich state of India. We have estimated that annual renewable contingency cost impact of 5 GW additional wind in Tamil Nadu to be in the range of US$27–76 million pa. Planning analysis should embrace the concept of renewable contingency to recognize these costs and put in place necessary spinning reserve and back-up generation resources.

The Use of Probabilistic Forecasts: Applying Them in Theory and Practice

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations for deploying new resources. Planning for fuel delivery and maintenance relies on subseasonal to seasonal forecasts. On shorter timescales of days, the weather affects both energy demand and supply. Electrical load depends critically on weather because electricity is used for heating and cooling. As more renewable energy is deployed, it becomes increasingly important to understand how these energy sources vary with atmospheric conditions; thus, predictions are necessary for planning unit commitments. On the scales of minutes to hours, shortterm nowcasts aid in the real-time grid integration of these variable energy resources (VERs).

Modeling High Wind Speed Shut-Down Events Using Meso-Scale Wind Profiles and Survival Analysis

Optimal siting is of paramount importance for managing the grid impacts of variable wind energy resources, especially in view of the increasing penetration of wind energy as part of the renewable energy mix. High variability of the renewable energy generation component requires that the system operator respond to generation deficits through frequent cycling of conventional generation and increased use of expensive peaking generation plants. Limited research has been conducted into methodologies whereby which the frequency, severity and relative risk of the loss of generation events can be quantified. This paper proposes a technique to empirically evaluate the risk of loss of generation events introduced by a singular site based on its simulated, or measured, historicity. This is accomplished through the statistical translation of meso-scale wind speeds into a number of wind speeds representative of those experienced by the turbines at a wind farm. An event series is derived describing the potential number of turbines in high wind speed shut down mode at the wind farm. Once the event series is obtained, survival analysis and the Cox proportional hazards model are utilized to characterize a site and contrast multiple sites in terms of relative risk. The methodology is demonstrated for high wind speed shutdown events, but can be applied for any event of interest, such as ramp-rate exceedances and drop below cut-in.

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Baseload electricity and hydrogen supply based on hybrid PV-wind power plants

Abstract The reliable supplies of electricity and hydrogen required for 100% renewable energy systems have been found to be achievable by utilisation of a mix of different resources and storage technologies. In this paper, more demanding parameter conditions than hitherto considered are used in measurement of the reliability of variable renewable energy resources. The defined conditions require that supply of baseload electricity (BLEL) and baseload hydrogen (BLH2) occurs solely using cost-optimised configurations of variable photovoltaic solar power, onshore wind energy and balancing technologies. The global scenario modelling is based on hourly weather data in a 0.45° × 0.45° spatial resolution. Simulations are conducted for Onsite and Coastal Scenarios from 2020 to 2050 in 10-year time-steps. The results show that for 7% weighted average cost of capital, Onsite BLEL can be generated at less than 119, 54, 41 and 33 €/MWhel in 2020, 2030, 2040 and 2050, respectively, across the best sites with a maximum 20,000 TWh annual cumulative generation potential. Up to 20,000 TWhH2,HHV Onsite BLH2 can be produced at less than 66, 48, 40 and 35 €/MWhH2,HHV, in 2020, 2030, 2040 and 2050, respectively. A partially flexible electricity demand at 8000 FLh, could significantly reduce the costs of electricity supply in the studied scenario. Along with battery storage, power-to-hydrogen-to-power is found to have a major role in supply of BLEL beyond 2030 as both a daily and seasonal balancing solution. Batteries are not expected to have a significant role in the provision of electricity to water electrolysers.

Maximum Loadability of Islanded Microgrids With Renewable Energy Generation

A microgrid (MG) is a small-scale power system which is fed by constrained distributed generation (DG) units and its continuous operation is affected by the variability of available generation resources. In this paper, a global sensitivity analysis (GSA) method is proposed to evaluate the impact of variable energy resources on the maximum loadability of islanded MGs (IMGs). First, a probabilistic optimization problem is formulated to calculate the IMG load margin considering the droop characteristics of DG units and uncertainties of renewable generation, loads and distribution feeder parameters. Next, the global sensitivity (GS) is introduced that can identify the impact of independent and correlated variables on the IMG loadability. Last, the sparse polynomial chaos expansion method is used to obtain the probabilistic models for IMG load margins, and an efficient GSA method is proposed to calculate the GS of IMG loadability to prevailing variables. The probabilistic models are considered for IMG input variables and the impact of variable correlations on IMG loadability is analyzed. The proposed method for calculating the maximum loadability is tested using a 33-bus IMG and the results are compared with those of other GSA and local sensitivity analysis methods.

Flexible Carbon Capture and Utilization technologies in future energy systems and the utilization pathways of captured CO2

Future 100% renewable energy systems will have to integrate different sectors, including provision of power, heating, cooling and transport. Such energy systems will be needed to mitigate the negative impacts of economic development based on the use of fossil fuels, but will rely on variable renewable energy resources. As two-thirds of global greenhouse gas emissions can be attributed to fossil fuel combustion, decarbonization of energy systems is imperative for combating the climate change. Integrating future energy systems with CO2 capture and utilization technologies can contribute to deep decarbonization. As these technologies can be operated flexibly, they can be used to balance the grid to allow for high levels of variable renewable energy in the power mix. The captured CO2 can be either utilized as a feedstock for various value-added applications in the chemical industry and related sectors such as the food and beverage industries. This paper reviews the state-of-the-art literature on CO2 capture and utilization technologies, with an emphasis on their potential integration into a low-carbon, high-renewables penetration grid. The potential market size for CO2 as raw material is also elaborated and discussed. The review paper provides an insight to the development and the technological needs of different energy system sectors, as well the limitations, challenges and research gaps to the integration of the variable renewable energy sources and flexible carbon capture and utilization technologies.

Resource Adequacy in Electricity Markets With Renewable Energy

This paper presents a market-based resource adequacy assessment framework to analyze the system generation portfolio that results in a competitive market environment. In particular, we apply the modeling framework to investigate the impact of high penetration levels of variable renewable energy resources and different market designs on achieving resource adequacy. The model captures the strategic capacity investment and retirement decision-making of profit-maximizing generation companies. In contrast to many previous studies, this paper considers markets for capacity, energy, and reserve products in detail; thus, the modeling framework allows improved analysis of generation expansion and revenue sufficiency in a competitive market environment. The modeling framework is based on Stackelberg leader–follower games and is formulated as a bi-level optimization problem, which is then transformed into a mathematical program with equilibrium constraints. Furthermore, we employ a Lagrangian decomposition algorithm to enhance computational performance. In a case study of the electric reliability council of texas (ERCOT) system, we compare the results from the proposed model with those obtained from a traditional centralized least-cost planning model. The results demonstrate the effectiveness of the proposed modeling framework and highlight the importance of considering strategic behavior in a competitive market framework when assessing resource adequacy.

The 2017 ISO New England System Operational Analysis and Renewable Energy Integration Study (SOARES)

The bulk electric power system in New England is fundamentally changing. The representation of nuclear, coal and oil generation facilities is set to dramatically fall, and natural gas, wind and solar facilities will come to fill their place. The introduction of variable energy resources (VERs) like solar and wind, however, necessitates fundamental changes in the power grid’s dynamic operation. VER forecasts are uncertain, and their profiles are intermittent; thus requiring greater quantities of operating reserves. This paper describes the methodology and the key findings of the 2017 ISO New England System Operational Analysis and Renewable Energy Integration Study (SOARES). This study was commissioned by the ISO New England stakeholders to investigate the effect of several scenarios of varying generation mix on normal operating reserves. The project was conducted using the holistic assessment approach called the Electric Power Enterprise Control System (EPECS) simulator. The study finds a minimal impact on current normal operating conditions in the ISO-NE system for scenarios with relatively low penetration of VERs. Nevertheless, for scenarios with a significant presence of VERs, the system may require additional amounts of both upward and downward load following reserves and upward and downward ramping reserves to effectively maintain reliable operations. In these scenarios, the curtailment of semi-dispatchable resources also becomes an integral part of balancing performance; in part to complement operating reserves and in part to mitigate the topological limitations of the system. Indeed, the integration of significant amounts of VERs in relatively remote regions significantly increases the potential of congestion on several key interfaces. In many of these scenarios, the system experiences heavy saturations of regulation reserves and their increase would enhance the response to residual imbalances. The concludes with final insights into the emerging roles of curtailment, energy storage, and demand response as integral parts of normal balancing performance.

Review and assessment of the different categories of demand response potentials

Demand Response (DR) is a well-known concept which has been recognized as an increasingly valuable tool to provide flexibility to the power system, to support the integration of Variable Renewable Energy (VRE) resources and to manage the grid more efficiently. In recent years, there have been a growing number of publications focusing on the estimation of different categories of DR potentials (e.g. theoretical, technical, economic, and achievable) using different methodologies and assumptions in each research study. The contribution of the present study is twofold. Firstly, a literature review is undertaken focusing specifically on the categorization of the scientific approaches used to estimate the different categories of DR potentials. To the best of authors' knowledge, a general procedure for the estimation of each DR potential category is still missing. Therefore, a novel user-friendly and step-by-step theoretical framework for the determination of the different categories of DR potentials is presented. Findings of this study reveal that literature has extensively focused on the estimation of the technical DR potential followed by the economic, theoretical and achievable potentials respectively. A lack of understanding of the different categories of DR potentials was also identified, which sometimes have been unduly used in the literature. The proposed framework is supported on a small sample of numerical approaches and equations which results in a structured approach to bringing consensus to the DR potential assessment.

Rethinking the electricity market design: Remuneration mechanisms to reach high RES shares. Results from a Spanish case study

Electricity systems experience a period of transition towards decarbonisation and face multiple uncertainties. Variable renewable energy resources undergo a rapid cost decline while policy makers push for stricter decarbonisation targets to limit global warming and to comply with international commitments such as the 2015 Paris Agreement. In this context, a better understanding on how today's electricity market design has to be modified to comply with high shares of variable RES generation is required. This work demonstrates the need to extend the current electricity market design by additional remuneration mechanisms to reach imposed quotas of renewable generation and provide investment incentives for new firm capacity. A Spanish case study presented in this paper explores the electricity system transition between 2025 and 2040. An electricity system resource expansion model (SPLODER) is used to study different policies and estimate the evolution of investments and costs over the transition period. Results indicate that the interactions between energy market prices, additional capacity and RES remuneration mechanisms are particularly sensible to policy decisions, demand growth and technological developments. Conclusions indicate that such remuneration mechanisms must account for the uncertainty of future electricity market developments and additional hedging alternatives are required to ensure the cost recovery of new generation technologies.

Complementarity assessment of south Greenland katabatic flows and West Europe wind regimes

Current global environmental challenges require vigorous and diverse actions in the energy sector. One solution that has recently attracted interest consists in harnessing high-quality variable renewable energy resources in remote locations, while using transmission links to transport the power to end users. In this context, a comparison of western European and Greenland wind regimes is proposed. By leveraging a regional atmospheric model specifically designed to accurately capture polar phenomena, local climatic features of southern Greenland are identified to be particularly conducive to extensive renewable electricity generation from wind. A methodology to assess how connecting remote locations to major demand centres would benefit the latter from a resource availability standpoint is introduced and applied to the aforementioned Europe-Greenland case study, showing superior and complementary wind generation potential in the considered region of Greenland with respect to selected European sites.

Data-Centric Hierarchical Distributed Model Predictive Control for Smart Grid Energy Management

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the hierarchical distributed model predictive control (HDMPC) to solve the energy management problem in the multitime frame and multilayer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized supervisory management (SM) layer and another short time-horizon during high-power variability for a distributed coordination management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically and experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.

Scaling-up Sustainable Energy Storage in Developing Countries

Background: The modularity and universal deployability of certain energy storage and variable renewable energy resources make the combination of these two elements a possible game changer for achieving universal access to electricity in developing countries while simultaneously decarbonizing their electric grids. Recent cost declines in electrochemical batteries have enabled solutions based on batteries and renewables that have proved to be cost-competitive with fossil-based alternatives in a growing number of cases. However, most widely-available battery systems may not be optimal for power systems applications operating under the challenging conditions frequently found in developing countries. Additionally, scaling-up sustainable energy storage in developing countries requires addressing a number of important challenges that are not well understood. The study presented in this article aims at identifying these challenges, and was undertaken in preparation of the Energy Storage Partnership: a consortium of over 30 organizations convened by the World Bank to jointly address them. Methods: Expert elicitation in combination with a literature review of standards, news articles, vendors’ public materials and academic literature. Results: The study identifies current challenges for scaling up energy storage in developing countries, and presents research and development work to overcome them. Conclusions: A wide spectrum of research and development actions is required for energy storage to make its full contribution to energy policy objectives in developing countries. Implementing the actions highlighted in this article will require a concerted approach by national governments and stands to benefit substantially from international cooperation.

Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study

The quest for economic growth has led to increase in world's energy consumption and to meet this continuously widening energy gap, high penetration of variable energy resources (VER) into the energy mix of countries all over the world is being encouraged. However, there is a limitation on the capacity of existing transmission facilities and the power system is more susceptible to voltage instability issues and the number of outages caused by voltage collapse has increased greatly. In this paper, a capacity factor based approach for optimal penetration of utility-scale PV solar power into the Nigerian power system considering voltage stability is discussed and compared with the line loss minimization approach. Multi-objective particle swarm optimization algorithm on MATLAB® is used to compute sufficient capacity of solar PV that the power system can accommodate while maintaining the stability of the system. An additional constraint based on voltage stability margin is included to keep the system within the stability margin. The proposed approach with the added constraint is found to perform better for both line loss reduction and voltage stability improvement than the line loss minimization approach. Two optimization algorithms are used to verify the accuracy of the approach for techno-economic planning.

Integration of renewable energy resources from the perspective of the Midcontinent Independent System Operator: A review

Due to the abundance of wind resources in its footprint and favorable energy policies and economics, the Midcontinent Independent System Operator has seen an increase of wind energy to its fuel mix since 2006. With over 11,000 MW under its control and 7000 MW on the generation interconnection queue, MISO has begun to act with respect to understanding what higher levels of variable renewable energy resources would mean for its system.

The impact of planning reserve margins in long-term planning models of the electricity sector

The planning reserve margin is the predominant metric used in long-term planning models to ensure the resource adequacy of projected power systems. Considerable work has been done to estimate the contribution of variable renewable energy resources, such as wind and solar, to the planning reserve margin, but little work has been done to assess what planning reserve margin should be used in planning models. Typically, U.S.-based models use the North American Electric Reliability Corporation (NERC)-recommended reserve margin levels. However, historical reserve margins have often exceeded the NERC-recommended levels, suggesting that the use of NERC-recommended levels in planning models may negatively bias projected future capacity investments relative to real-world trends. Using the Regional Energy Deployment System capacity expansion model, we show that setting the planning reserve margin to observed levels in lieu of the NERC-recommended levels leads to substantial differences in near-term capacity additions. Scenarios using alternative specifications for the reserve margin resulted in increased national solar builds of 20–100 GW. Given that the magnitude on results from altering the reserve margin level is similar to or greater than many policies frequently analyzed with planning models, careful consideration of the reserve margin level is crucial for developing accurate power sector projections.

Multi‐stage co‐planning framework for electricity and natural gas under high renewable energy penetration

This study develops a new multi-stage (dynamic) approach for the co-planning of power and gas systems to deal with variable renewable energy resources (VREs). The model is formulated using a stochastic programming framework to accurately capture the unfolding of both short and long-term uncertainties faced by power and gas systems. The effects of high renewable energy penetration and renewable energy uncertainty in both systems are assessed while determining the optimal investment and operation decisions in several stages of the planning horizon. To prove the benefits of the proposed approach, the authors compare the results of the authors' framework with other methods used in the literature. The effectiveness of the framework is validated on a realistic case of Queensland, Australia, in which both networks are driven to capture the link between these systems and to accommodate the state's unique features of renewable availability. The results demonstrate that their dynamic approach provides more robust outcomes compared to other methods as it allows adapting the expansion plans to unexpected changes in the future. The analysis also shows that a transition towards renewable energy presents a higher cost, different investment strategies, and lower gas-fired consumption compared to the terminal renewable target.

Review of planning methodologies used for determination of optimal generation capacity mix: the cases of high shares of PV and wind

It is an undeniable fact that energy systems all over the world are at the point of a paradigm shift as a need for decarbonisation is eminent and unavoidable. The pressure to decarbonise mounts year after year. Since two thirds of all anthropogenic greenhouse-gas emissions come from the energy sector, decarbonisation is more about reducing emissions in the energy system than any other system in the world. The increased need for decarbonisation has resulted in the increased installation of photovoltaic (PV) and wind systems in countries such as China, India, Germany, Ireland, Denmark, Japan and USA. The increased use of intermittent renewable energy resources introduces a need for advanced methods of planning because traditional planning methods give sub-optimal generation capacity mix when the electric grid is faced with high shares of variable renewable energy resources such as PV and wind. In light of this, this review highlights the major changes in planning methodologies when solving for optimal penetration of generation capacity in systems with high shares of PV and wind. The major highlights are placed on why the methodologies need to evolve as penetration levels of PV and wind increase and further highlight missing issues from the current advanced methods.

Surrogate Affine Approximation Based Co-Optimization of Transactive Flexibility, Uncertainty, and Energy

This study presents an approach to co-optimization of transactive flexibility, energy, and optimal injection-range of Variable Energy Resource (VER). Flexibility receives immense attention, as it is the essential resource to accommodate VERs in modern power systems. With a novel concept of transactive flexibility, the proposed approach proactively positions the flexible resources and optimizes the demand of flexibility. A surrogate affine approximation (SAA) method is proposed to solve the problem with variable infinite-constraint range in polynomial time. It is shown that SAA is more optimistic than the traditional affine policy in the power literature. The SAA method is also applicable to the search for secure injection-range of VER, which is often heuristically determined in industry given the latest system information. In practice, VER generation beyond the secure injection-range has to be curtailed, even if its cost is lower than the marginal price. The proposed technique helps accommodate more VERs securely and economically by increasing secure injection-range. The model and the solution approach are illustrated in the 6-bus system and IEEE 118-bus system.