Variable Renewable Energy Sources Research Articles

Reduction of effective cost for seawater desalination using floated solar energy in south of Jordan Aqaba Port

The major issue that prevails all over the world is potable water. Only 1% of all Earth’s water exists as liquid, whereas less than 3% as freshwater, mostly glaciers, and 97% as salt water. Desalination is the process of removing salt from sea water in order to obtain fresh water. Nowadays, scientists are exploring seawater desalination for potable water. Another key concern that exists in any country is clean energy. The energy crisis is due to overconsumption of fossil fuels such as oil, gas, and coal. Engineers try various sources of renewable energy. The cost of potable water and clean energy, say, the cost pertaining to capital, operation, maintenance, performance, and fuel, has increased year after year. The technologies for generating renewable energy, like solar and wind, do not have fuel costs. Modern desalination techniques incur a high initial investment and a higher maintenance cost. The life-cycle cost per litre of potable water generated by the solar stills is lower than that of conventional methods. The most cost-effective way to power desalination plants is with solar power. Seawater desalination with the use of solar power is employed in these regions as a viable alternative solution. Jordan is a country in western Asia. Water and energy sources are scarce in Jordan, with water availability in Jordan is estimated to be less than 100 m3 per year. Jordan imports 95% of its energy needs. Jordan plans to generate 14% of the country’s electricity by utilizing wind and solar energy. The use of solar stills in Jordan can reduce the dependency on oil and gas for power generation. The use of floating solar stills for seawater desalination represents a promising tool for Jordan. Solar stills work on the evaporation and condensation processes. They distil water by using the heat of the sun. Efficiency and productivity have been measured to quantify the performance of solar stills. Solar stills have the advantages of simplicity, low cost, ease of maintenance, and low environmental impact. In this paper, an attempt is made to analyze the types, materials, components, design, performance, cost, benefits, advantages, and limitations of solar stills. The paper also discussed the reduction of the effective cost of seawater desalination using floated solar energy in Jordan's Aqaba Port.

Optimizing Renewable Energy Management in Smart Grids Using Machine Learning

Renewable energy management in smart grids is a challenging problem due to the uncertainty and variability of renewable energy sources. To improve the efficiency and reliability of renewable energy utilization, various optimization techniques have been proposed. In this paper propose an approach based on the Extreme Learning Machine (ELM) algorithm with Particle Swarm Optimization (PSO) for optimizing renewable energy management in smart grids. The ELM algorithm is used to model and predict renewable energy generation, while the PSO algorithm is used to optimize the parameters of the ELM algorithm. The proposed approach is evaluated on a dataset of solar energy production and compared with other optimization techniques. The results show that the ELM-PSO approach can improve the accuracy of renewable energy predictions and reduce energy costs in smart grids. The proposed approach can be used in various renewable energy systems, such as wind turbines, solar panels, and hydroelectric power plants, to improve the efficiency and reliability of renewable energy utilization.

Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts

The rapid integration of variable renewable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process such as Dynamic Assessment and Determination of Operating Reserve ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DynADOR</uri> ), Dynamic Reserves Dimensioning ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DRD</uri> ), and <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RESERVE</uri> , among others. While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the California Independent System Operator (CAISO) system. Results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.

Allocation of Spinning Reserves in Autonomous Grids Considering Frequency Stability Constraints and Short-Term Solar Power Variations

Low-inertia, isolated power systems face the problem of resiliency to active power variations. The integration of variable renewable energy sources, such as wind and solar photovoltaic, pushes the boundaries of this issue further. Higher shares of renewables requires better evaluations of electrical system stability, to avoid severe safety and economic consequences. Accounting for frequency stability requirements and allocating proper spinning reserves, therefore becomes a topic of pivotal importance in the planning and operational management of power systems. In this paper, dynamic frequency constraints are proposed to ensure resiliency during short-term power variations due to, for example, wind gusts or cloud passage. The use of the proposed constraints is exemplified in a case study, the constraints being integrated into a mixed-integer linear programming algorithm for sizing the optimal capacities of solar photovoltaic and battery energy storage resources in an isolated industrial plant. Outcomes of this case study show that reductions in the levelized cost of energy and carbon emissions can be overestimated by 8.0% and 10.8% respectively, where frequency constraints are neglected. The proposed optimal sizing is validated using time-domain simulations of the case study. The results indicate that this optimal system is frequency stable under the worst-case contingency.

Importing from? Capacity adequacy in a European context

In this paper we ask the question: why has capacity adequacy become a worry in developed countries in general, and Europe in particular, in recent years, and how serious is this problem? While the liberalisation process and the fast move to variable renewable energy sources play a role in this process, they are not the only causes. We discuss the extent to which regulators and policy makers are able to influence the evolution of the market and also consider the role of and challenges created by regional markets and cross-boarder integration. We conclude that policy makers can go into one of two directions. The first option is to ignore cross-border trading in their planning. While this would create security of supply for individual countries, it would come at the cost of very significant investment subsidies. The alternative is to rely on cross-border trade, an option which requires not only close cooperation and coordination, but also significant trust between the jurisdictions and institutions involved, to ensure sufficient capacity. The choice between these options is a political decision, to be made at the national and supranational levels, not by regulators, and even less by market participants.

The Role of Optimal Transmission Switching in Enhancing Grid Flexibility: A Review

The integration of variable renewable energy sources (RES) into power grids has resulted in a more complex operating environment for power system operators (SO), necessitating the need for increased grid flexibility. This is crucial for utilities, as the cost-effective utilization of existing infrastructure is necessary, given the prolonged construction period and high investment costs associated with building new power delivery facilities. Under the umbrella of smart grid, optimal transmission switching (OTS) is a cost-effective transmission technology that can alleviate concerns related to network congestion, limited transmission capacity, and high penetration of renewables. OTS is a concept that is integrated into the optimal power flow (OPF) problem such that it provides SO the choice to temporarily switch one or more lines out of service from the network. OTS is shown in the literature to have economic benefits, reduce operational cost, relieve network congestion, serve as a corrective mechanism for reducing voltage violations, improve system reliability, and minimize system loss. Despite the significant attention given to OTS by researchers over the past decade, no extensive review paper exists on the topic in the literature. Therefore, this paper provides a state-of-the-art overview of the OTS problem. The concept of OTS is explained by approximating the alternating current OPF problem into a linear direct current OPF problem. Various alternative models of the OTS problem proposed in the literature are discussed. The paper also analyzes the interaction between OTS and other flexibility options such as Dynamic thermal rating (DTR), Energy storage systems (ESS), and RES. Furthermore, the paper presents the general framework and impact of the short-term OTS problem on long-term expansion planning problems. Finally, an extensive literature review on the impact of OTS on system reliability is provided.

The dynamic complementarity of renewable energy sources: A Bayesian vector autoregressive approach

ABSTRACT The climate factors are the main driving forces of variability of renewable energy sources. In facing the safety challenges of the large-scale grid integration of renewable energy, it is important to understand the effects of climate shocks on the generation of the renewables as well as if the complementarity among different types of renewable energy exist and could be employed to smooth out the production and better match with the load. We employ a Bayesian vector autoregressive (BVAR) model along with impulse response function and forecast error variance decomposition to investigate the dynamic complementarity among renewable energy in the face of different climate shocks. The proposed methodology is applied over Qinghai employing the power generation data as well as meteorological data. The results show that there is greater general complementarity between solar power and hydropower. Wind power and solar power, and wind power and hydropower, exhibit dynamic complementarity when there are fluctuations in precipitation and wind speed, respectively. China should make efficient use of the natural complementarity advantages between renewable energy sources, carry out joint optimal dispatching, and accelerate the construction of a clean, low-carbon, safe and efficient energy system.

Advanced Spatial and Technological Aggregation Scheme for Energy System Models

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations’ impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of &lt;5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42%, but the run time is reduced by 92.95%.

Levelling the Photovoltaic Power Profile with the Integrated Energy Storage System

The extensive penetration in the energy mix of variable renewable energy sources, such as wind and solar, guarantees boosting of the transition toward a decarbonized and sustainable energy system as well as tackling of climate targets. However, the instability and unpredictability of such sources predominantly affect their plant production. Thus, utility-scale energy storage is required to aid in balancing supply and demand and, as a result, to prevent unbalances that might cause issues at different grid levels. In the present study, the authors’ patented energy storage technology, known as Integrated Energy Storage System (I-ESS), is combined with a 10 MWp solar plant. The PV plant and the I-ESS unit function as a Virtual Power Plant (VPP). The selected VPP management strategy attempts to optimize the daily hours during which the plant supplies steady power output. Numerical simulations show that the VPP plant can effectively smooth the PV peak and manage the power supply. In particular, by the definition of a novel metric expressing the ratio between regular hours of power provided to the grid plus the energy stored in the backup unit and the total number of hours in a year, the results show that the VPP regularity is relatively high in terms of PV output, ranging from a low of 50% in December to a high of 87% in August. Thus, the proposed VPP arrangement seems to be a promising technology for pushing toward the carbon-neutral transition.

Power-to-heat plants in district heating and electricity distribution systems: A techno-economic analysis

This paper investigates the Power-to-Heat energy conversion process carried out by heat pumps connected to a district heating network used to provide heat to the heating sector and, at the same time, to provide flexibility for the electricity sector. The aim of this work is to analyze from a techno-economic point of view the flexibility potential of this solution used to absorb the over-generation of Variable Renewable Energy Sources in the distribution system. A scenario, based on the electric distribution system and the district heating distribution system of the city of Turin, was created to carry out this type of study. The results showed that, flexible use of these Power-to-Heat systems can be exploited to shift part of electric loads in periods of renewable over-generation, with significant benefits from an economic point of view. Furthermore, the position of Power-to-Heat systems within an electricity distribution network has a significant impact; to make the most out of the flexibility of these plants, they should be placed in those areas of the network that present the greatest local over-generation of renewables. The use of heat pumps will be necessary for the energy transition thanks to the high conversion efficiency. However, in order to fully exploit all the benefit that this technology can offer its flexibility cannot be ignored.

Power sector decarbonization planning considering renewable resource variability and system operational constraints

Bottom-up long-term planning models generally consider low levels of spatial, temporal, and technical details to reduce associated computation. However, these details are required to capture spatial and temporal variability of Renewable Energy Sources (RESs) and perform harmonized dispatch of conventional generating units. In their absence, models may overestimate RESs uptake and underestimate the quantum of required flexible resources. This may create a false impression of sector decarbonization and the obtained capacity mix may result in frequent RES or load curtailment during real-time system operations. In this context, this study aims to improve upon the shortcomings of conventional planning approaches and develop a TIMES based planning model for the Indian power sector. The model adopts high spatial and temporal definitions of 5 regions and 288 sub-annual timeslices. RESs are categorized into sub-classes based on annual capacity factors to capture their intra-regional variations without further increasing the spatial resolution. Each month is represented by a day of chronological hourly resolution that facilitates the capture of RES temporal variations and incorporation of operational aspects of generating units such as minimum stable generation, ramp limits and partial load operation. The impact of these operational parameters on system planning is analysed to identify a trade-off between planning accuracy and computational complexity. The study is relevant for countries that are rigorously following their decarbonization plans with high penetration of variable RESs and have a similar trajectory as that of India. The results indicate that decarbonization levels can be better assessed if capacity mix and flexible resources are adequately valued by considering operational parameters at the planning stage for a power system dominated by low flexible generation.

Installation and Performance Study of a Vertical-Axis Wind Turbine Prototype Model

Amongst various sources of renewable energy, the kinetic energy of blowing wind has environmental friendliness and easy availability, together with other benefits. The wind energy is converted into usable electrical energy by means of a robust device termed a wind turbine. To carry out a performance study of such a device, a small-scale model vertical-axis wind turbine was installed at the laboratory and was run by artificial wind energy produced by a pedestal fan for low and medium speeds and a blower for higher speeds. The variation in critical parameters such as output power and voltage with different speeds was studied. The average output power and voltage were observed to increase with average shaft speed with linear and curvilinear patterns, respectively. The vibration produced at the bearing shaft resulting from the rotating components was analyzed as well. As observed, the peak values of critical vibration parameters such as displacement, velocity, acceleration, and frequency mostly varied curvilinearly with average shaft speeds. To study the applicability of the power generation, an electronically controlled automatic drip irrigation system was allowed to run by the wind turbine and important observations were made. Theoretical analyses (numerical and analytical) of the wind flow and power generation were also performed.

Optimalno planiranje balansne rezerve za pokrivanje debalansa u proizvodnji vetroelektrana u Južnom Banatu

- Balancing of variable renewable energy sources is one of the main problems of integrating renewable energy sources into the power electricity system. This paper performs a statistical analysis of the dependence of the error in the prediction of wind farm production in the region of southern Banat, on the forecasted power. The main contribution of the paper is the proposal of the methodology for the calculation of the required balance reserve in the electric power system of Serbia for day ahead for the existing condition of the installed wind power plants that are connected to the transmission network. All stakeholders have practical significance and benefit from the proposed methodology, the wind farm owner who can plan the necessary lease of balancing capacities, the transmission system operator who can plan uncertainties in wind farm production and the balancing responsible party that should plan balancing capacities.

Urban vertical farming with a large wind power share and optimised electricity costs

Producing food in an environmentally sustainable way for the growing human population is a challenge to the global food system. Vertical farm (VF) as a part of the solution portfolio is attracting interest since it uses less water, pesticides, and land which are scarce in many parts of the globe. Despite these positive factors, the energy demand for vertical farms is high, and farms often remain separate and excluded from cities where most of the population lives. City-level energy system solutions exist to empower energy efficiency and increase the share of variable renewable energy sources, but their potential has not yet been estimated for an urban energy system that includes large vertical farms. Accordingly, in this study, we simulate an urban energy system that practices vertical farming with large-scale variable renewable energies and flexibility measures. For the first part of the study, we modelled a vertical farm's energy system with demand response control to maximize electricity cost savings. To evaluate the potential of demand response, the analysis is carried out for different crops (lettuce, wheat, and soybean), and different electricity price profiles. The result of demand response control can be a reduction of 5% to 30% in electricity consumption costs. Further, sensitivity analyses highlight the effect of electricity price variations and photoperiod on demand response outcomes. In the second part, the operation of an urban energy system (Helsinki, Finland) with vertical farms was analysed through two different scenarios. These scenarios represent the emission-free Helsinki energy system in 2050 with large-scale wind power implementation. As VFs can use electricity outside the peak demand hours, the inclusion of VF with the right energy system configuration can improve the power consumption within the system by up to 19%. Further, we show that connection to the exogenous power market is important to support vertical farming in the future energy systems. In this study, key points in the integration of VF in urban energy systems are highlighted, including the role of exogenous power markets, the potential for increasing local energy consumption with large wind power, and the importance of crop selection in reducing VF's energy costs through demand response. In a city-level solution with a high wind power share, we thus recommend including a vertical farm side by strong sectoral coupling as part of the future design to maximise local consumption.

Quantifying value and representing competitiveness of electricity system technologies in economic models

Evaluating competition between electricity technologies is challenging because it depends on both their costs and their values. While technology costs can typically be estimated from projections of the cost components—capital, fuel, and O&M—estimating a technology’s value is more complex due to its dependence on its contributions to multiple different grid services, each with prices that can vary substantially over space and time. In this work, using an electricity model of the contiguous United States, we develop relationships between relative value and share of total generation for major electricity generation technologies which, when paired with projections of technology costs, can be used to estimate technology competitiveness. We identify significant differences in the relationship between relative value and generation share for variable renewable energy (VRE) and non-VRE sources, but we demonstrate that all technologies require consideration of their dynamic values (in addition to cost) when evaluating competitiveness. In addition, we demonstrate that relative value of a technology is substantially impacted by not only its own generation share but also other aspects of the system state, in particular the mix of other technologies present in the system. Finally, we use the developed relative value relationships in combination with projections of future technology costs in a coarse resolution model that competes technologies based on a comprehensive competitiveness metric: profitability-adjusted LCOE (PLCOE). We show that this simple representation of technology competition approximately recovers the generation mix from a detailed model, which is not possible using LCOE alone. Such an approach can be used to improve the representation of technology competition in coarse-resolution models such as integrated assessment models, for which simplified metrics are often needed.

On the aggregation of input data for energy system models

The increasing share of variable renewable energy sources in power systems poses new challenges to policy makers and network planners alike because of the sources’ variability and insufficient energy storage capability. This requires the development of new optimization models that consider the inter-temporal connection between different periods aggregating them on a more general level (e.g., days or weeks). However, aggregation models are often empirical and based on common clustering algorithms. In this paper, we carry out a numerical exploration of the relationship between the structure of the system and the hyperparameters required for these aggregation procedures. Our findings indicate that there is valuable information from the power system that can be used to improve the aggregation. This is important because, in most cases, the accuracy of the aggregation cannot be measured exactly.

A Dual-Stage Controller for Frequency Regulation in a Two-Area Realistic Diverse Hybrid Power System Using Bull–Lion Optimization

In this article, a dual-stage proportional integral–proportional derivative with filter (PI–PDF) controller has been proposed for a hybrid two-area power system model having thermal-, hydro-, gas-, wind-, and solar-based power generating sources. Superconductor magnetic energy storage (SMES) units to cope with the transient power deviations have been incorporated in both areas. Governor dead-band (GDB) is considered in the governor model of thermal, and a generation rate constraint (GRC) is considered in the thermal and hydro turbine models to analyze the impact of system nonlinearity. The parameters of the proposed control strategy are optimally tuned by deploying a newly developed bull–lion optimization (BLO) to maintain optimal frequency and power response during system load deviations. Variations in wind speed and PV solar irradiance data have been included to examine the effectiveness of the BLO-based PI–PDF controller with system uncertainties and variability of renewable energy sources. The obtained results are validated by comparison with recently developed existing optimization techniques. The results revealed that the proposed control strategy is efficient for regulating the frequency and tie-line power of renewable integrated power systems. Further, the BLO-based PI–PDF control strategy improved the performance in terms of performance indices like settling time and peak overshoot/undershoot in wide scale.

Impact of district heating and cooling on the potential for the integration of variable renewable energy sources in mild and Mediterranean climates

Europe’s decarbonisation ambitions can’t be achieved without the full and rapid decarbonisation of its buildings which represent over 36% of its greenhouse gas emissions, the majority of which are linked to space heating and domestic hot water preparation. District heating, and in recent times, cooling must play a large role in satisfying this demand, especially in densely populated urban areas which already hold over half of the World’s population. Besides the decarbonisation potential linked to heating and cooling production, these systems hold a strong potential for increased flexibility in the power sector using power to heat technologies thus increasing the potential for the utilization of intermittent renewables such as wind and solar. The goal of this research is to demonstrate the impact district heating and cooling can have on the potential for the utilization of intermittent renewable electricity sources in mild and Mediterranean climates, which traditionally have lower shares of district energy systems. Additionally, this paper presents the newly implemented capabilities of the H2RES linear optimization tool to model district cooling systems alongside the existing capacity to model district heating systems. The results demonstrate a significant capacity of district heating and cooling systems to act as demand response tools thus greatly increasing the potential for the utilization of wind and PV for electricity generation. In some scenarios, up to 73% of the total electricity demand could be covered with wind and PV and a production of excess electricity of only 5% on an annual basis. The Republic of Croatia has been used as a case study for this research.

Improving Pumped Hydro Storage Flexibility in China: Scenarios for Advanced Solutions Adoption and Policy Recommendations

The decarbonisation targets of the People’s Republic of China are ambitious. Their achievement relies on the large-scale deployment of variable renewable energy sources (VRES), such as wind and solar. High penetration of VRES may lead to balancing problems on the grid, which can be compensated by increasing the shifting flexibility capacity of the system by integration with energy storage, e.g., by installing additional electricity storage. Pumped Hydro Storage (PHS) is the most diffused electricity storage technology at the global level and the only fully mature solution for long-term electricity storage. China already has the highest PHS capacity installed worldwide and plans to increase it strongly before 2030. The present study, based on the data from the “Pumped Storage Tracking Tool” of the International Hydropower Association, investigates the potential for technological improvement of the existing and future PHS fleet in China. The aims of adopting advanced PHS solutions allow China to better cope with the task of balancing the VRES production. The potential for adopting advanced PHS solutions is evaluated through five different intervention possibilities (here referred to as scenarios). These scenarios consider revamping part of the operational Pumped Storage Plant (PSP) fleet and redesigning future installations that are already planned. As a result, considering all the major technical and authorisation process constraints, 4.0% (5.2 GW) of the 132 GW fleet expected to be commissioned before 2035 could additionally adopt advanced PHS in a high-potential scenario. Meanwhile in the medium and low potential scenarios, the quota can reach 11.1% (14.6 GW) and 26.2% (34.5 GW), respectively. Furthermore, policy recommendations are elaborated to promote, facilitate, and support the adoption of these advanced PHS solutions.

The effect of spatial granularity on optimal renewable energy portfolios in an integrated climate-energy assessment model

Renewable energy planning is key to achieving target levels of renewable-energy penetration and electricity demand in the European Union. Mean–variance analysis can be used to identify the optimal spatial and technological deployment of variable renewable energy (VRE) sources in terms of maximizing VRE penetration and minimizing supply risks. We investigate the extent to which optimizing capacities at the scale of climate-data grid points, instead of administrative regions (a common approach due to data availability and computation costs), helps generate more optimal renewable deployment scenarios. A finer description of climate resources, and thus the VRE capacity factors, results in a better exploitation of complementarities, partly due to the increased degrees of freedom in the optimization. A detailed analysis of the causes behind these improvements shows that better describing local conditions leads to two advantages over less granular counterparts: higher average capacity factors and generation combinations that offer lower covariances. This analysis also reveals that more granular approaches significantly reduce variability in daily and annual climate frequencies in renewable generation under the optimal scenario. These results provide evidence of the need to account for detailed climate information to accurately identify optimal renewable deployment scenarios and support stakeholders and policy makers when it comes to making sustainable commitments.