Hourly Demand Research Articles

Saving gas through cross-border renewable and nuclear electricity generation

Abstract In fossil fuel-driven electricity markets, gas power plants are mostly the price setting technology in peak demand hours. At the same time, gas technology has the role of balancing out the volatility of increasing shares of intermittent renewable generation. This interdependence, coupled with recent fears of supply shortages, raises the question how gas-fired generation can be replaced with carbon-free technologies. Controlling for fundamental supply and demand in the interconnected markets, we quantify the effect of intermittent renewable and nuclear generation in neighboring markets on the use of natural gas for electricity generation in Germany. Findings indicate that cross-border intermittent wind, PV, and nuclear generation significantly reduce the need for German gas. We further show Germany’s dependency on French cross-border nuclear energy to supplement the gas unavailability during the gas crisis period. We argue for larger market coupling and flexible generation alternatives and adaptive demand solutions including cross-border capacities for substituting gas when renewable generation is low across Europe.

Subway Multi-Station Coordinated Dynamic Control Method Considering Transfer Inbound Passenger Flow

The prominent contradiction between passenger demand and capacity in rush hours at subway stations causes inconveniences to travel and even leads to safety risks. Existing research on the cooperative control of passenger flow at stations mostly focuses on a single direction, rarely considering transfer passenger flow control. This study formulated a coordinated dynamic control strategy for multiple stations in both directions as a deterministic mathematical programming model to optimise the crowded passenger flow. The optimisation objectives were set as the warning levels of crowded passenger flow and the detention time of all passengers. The constraints included limitations on station service capacity, train capacity, and the number of people boarding trains. Additionally, considering separate control over the transfer inbound passenger flow at transfer stations, an upward- and downward-direction coordinated dynamic control model was constructed. Numerical experiments based on real-world data from the Nanjing Metro Line 1 were conducted to investigate the effectiveness of the proposed cooperative control scheme and evaluate its performance.

Low-emission hydrogen supply chain for oil refining: Assessment of large-scale production via electrolysis and gasification

The refining industry is a major hydrogen consumer, mainly relying on fossil fuel-derived hydrogen. While recent literature has focused on the production of hydrogen from water electrolysis, (waste) biomass gasification is another effective method for low-emission hydrogen production, and the refining industry is well positioned for its rapid adoption. This study conducts a techno-economic assessment of the whole supply chain of hydrogen for oil refining and analyses the LCA-climate change for residual biomass gasification and water electrolysis pathways. The potential economic and environmental benefits of combining both production methods are also analyzed. A real case study featuring four different scenarios, using data from a refinery, coupled with local data and future projections for potential curtailment and electricity prices, is included. The hourly hydrogen generation and demand profiles of the refinery were analyzed to accurately assess storage requirements. Results indicate that combining both technologies does not result in clear environmental or cost benefits, with residual biomass gasification emerging as the most advantageous configuration for the base case. Sensitivity analysis reveals that hydrogen produced via electrolysis may become more cost-effective if residual biomass prices are high enough. The importance of underground storage (salt cavern) is highlighted due to its low investment, while other storage methods can significantly increase the Levelized Cost of Hydrogen (LCOH). This study demonstrates that hydrogen production through gasification can be less carbon-intensive and more cost-competitive than electrolysis. Achieving low LCOH values and greenhouse gas emissions is feasible in all scenarios, indicating that both water electrolysis and residual biomass gasification are economically viable options for contributing to a low-emission global energy system.

Optimum carbon tax rate for emission targets of electricity generation system by life cycle techno-economic-environmental optimization model

Reduction of greenhouse gas emissions from the electricity sector is of special interest for decarbonization of energy systems. Optimum carbon tax as a powerful policy tool is here studied by integrated techno-economic-environmental life cycle analysis in a unit commitment problem. The present model is formulated as a bi-level optimization problem to reach the optimum carbon tax rates for different emission targets. The total life cycle CO2 emissions are estimated to be 20333.90 tons/hr in a real case study of Iran's electricity sector for the peak demand hour. The carbon tax rate for a 2 % and a 3 % emissions reduction is equal to 15.87 and 24.65 $/ton, respectively. In the business-as-usual scenario, higher carbon taxes will be required each year to meet the same emission target. However, developing the electricity sector with cleaner technologies and strategic plans may decrease the carbon tax rate. The extent of this reduction depends on the type of development program implemented in the green revolution scenario. Therefore, policy makers play a direct role in ensuring the sustainable development of the electricity sector. The present model provides a robust framework to analyze the real carbon tax rates for the whole life cycle of electricity generation dispatch.

Using statewide transportation planning model to forecast demand for electric vehicle charging at stations along intercity highways

The availability of Charging Stations (CSs) with adequate capacity is critical for the growth in ownership and usage of Electric Vehicles (EVs). In the United States, the state Departments of Transportation (DOTs) are responsible for planning CSs along the Interstate, national, and state highways. While the state DOTs have their Statewide Transportation Planning Models (STPMs), most of these models do not represent EVs internally. The state DOTs can only rely on the existing STPMs to forecast the demand for EV charging at specific CS locations in a state’s highway network. This research proposes a methodology that utilizes an existing STPM, EV and non-EV vehicle registration data, and hourly traffic volumes at strategic counting stations to predict the hourly demand for EV charging (charging port occupancy) at proposed CSs. This approach first estimates the volume of all passenger cars that will pass the vicinity of a CS along intercity highways, and identifies the Traffic Analysis Zones (TAZs) of their trip origins. Based on the EV adoption rates of the TAZs, EV range, and EV owner’s charging behavior, the methodology forecasts the volume of EVs that pass the vicinity of a CS and the number of EVs that will stop at the CS to charge every hour of the day. An example problem demonstrated the application of the methodology in the State of Texas. It not only forecasted the hourly occupancy at CSs along three major corridors, but also demonstrated how to determine the capacities of the CSs. The challenges of using the existing STPM and their solutions are discussed. This proposed methodology enables the state DOTs to forecast the demand for EV charging and the capacities at the proposed CSs along intercity highways, thus allowing the DOTs to apply for federal funds to accelerate the deployment of the CSs.

Enhanced energy efficiency technologies in healthcare buildings: a case study of waste heat recovery from an optimized battery-integrated cogeneration system

Abstract Healthcare buildings face significant challenges in enhancing energy efficiency. Combined, heat and power (CHP) plants integrated with battery energy storage systems (BESS) offer a promising solution by meeting electric, thermal and cooling demands using a single fossil primary energy source. Starting from an optimized battery-integrated cogeneration plant, a comparative analysis of two waste heat recovery technologies in a hospital building context has been conducted in this study. Specifically, an ammonia-water absorption, power, and cooling (APC) system and an organic Rankine cycle (ORC) plant are examined, both powered by the waste heat recovered from exhaust gases of the natural gas internal combustion engine, operating in a cogenerative configuration. A control strategy is implemented to enhance the efficiency of the integrated systems, prioritizing either cooling or electric power production based on hourly demand within the hospital. The results highlight the integration of waste heat recovery technologies inside the optimized battery-integrated cogeneration is capable to reach a 20.1% and 18.82% in fossil primary energy saving (PES), respectively with the ORC and APC systems. However, the higher investment cost brings the simple payback (SPB) period of the configuration with the ORC plant to 3.5 years, compared to 3.3 years of the solution with the APC plant. The study shows the benefits of incorporating waste heat recovery technologies into cogeneration systems with energy storage system, providing valuable guidance for developing economically viable and eco-friendly solutions for managing energy in healthcare buildings.

Multi-Objetive Dispatching in Multi-Area Power Systems Using the Fuzzy Satisficing Method

The traditional mathematical models for solving the economic dispatch problem at the generation level primarily focus on minimizing overall operational costs while ensuring demand is met across various periods. However, contemporary power systems integrate a diverse mix of generators from both conventional and renewable energy sources, contributing to economically efficient energy production and playing a pivotal role in reducing greenhouse gas emissions. As the complexity of power systems increases, the scope of economic dispatch must expand to address demand across multiple regions, incorporating a range of objective functions that optimize energy resource utilization, reduce costs, and achieve superior economic and technical outcomes. This paper, therefore, proposes an advanced optimization model designed to determine the hourly power output of various generation units distributed across multiple areas within the power system. The model satisfies the dual objective functions and adheres to stringent technical constraints, effectively framing the problem as a nonlinear programming challenge. Furthermore, an in-depth analysis of the resulting and exchanged energy quantities demonstrates that the model guarantees the hourly demand. Significantly, the system’s efficiency can be further enhanced by increasing the capacity of the interconnection links between areas, thereby generating additional savings that can be reinvested into expanding the links’ capacity. Moreover, the multi-objective model excels not only in meeting the proposed objective functions but also in optimizing energy exchange across the system. This optimization is applicable to various types of energy, including thermal and renewable sources, even those characterized by uncertainty in their primary resources. The model’s ability to effectively manage such uncertainties underscores its robustness, instilling confidence in its applicability and reliability across diverse energy scenarios. This adaptability makes the model a significant contribution to the field, offering a sophisticated tool for optimizing multi-area power systems in a way that balances economic, technical, and environmental considerations.

An optimal demand side management for microgrid cost minimization considering renewables

AbstractIn an ordinary microgrid configuration, the required load changes from hour to hour. The power system firms determine the cost of energy at different times of day by considering the highest and lowest points of the consumption curve. This is referred to as time‐of‐use (TOU) pricing for power. The hourly basis load demand is divided into flexible and inflexible categories. Demand side management (DSM) lowers peak demand while rewarding customers for their participation based on load lowering. His rebuilds the whole load model on the pillars of demand cost movement. The research recommends a DSM methodology based on a combined intellect method to lower the total cost of employing loads in a microgrid (MG) structure while considering carbon tax as an unavoidable constraint to lower the emission of pollutants. This is because 40% of microgrid customers are willing to participate in the DSM scheme. The results obtained in each illustration demonstrate that the suggested DSM technique is suitable in terms of cost reduction. The generating cost was decreased from $15,488 to $15,354 when 0%–40% of clients engaged in the DSM programme. With just a 3% compromised increase in generation costs, a carbon price combined with economic emission dispatch reduced the pollutants emitted by up to 78%, from 70 to 15 tons.

Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms

In this work, a study of economic and emission dispatch issues based on the multi-objective optimization is solved, and generation costs and emissions are reduced by utilizing multi-objective optimization techniques. This optimization is carried out in an IEEE-30 bus system, with and without the integration of wind energy sources, with equality and inequality constraints. The equality constraints are the power balance constraints, stipulating that to have an optimal solution, the generated power must be adequate to satisfy the load demand plus losses. The inequality constraints are a collection of limitations for active power generation, reactive power generation, generator bus voltage, and load bus voltage. To track the hourly load demand, a daily load profile is established using the IEEE-30 bus system. The generation costs and emissions in the system are optimized using multi-objective particle swarm optimization and multi-objective Ant–Lion Optimization approaches. In order to determine the goals’ minimum values, a fuzzy min–max technique is applied. The values that have been minimized are then compared to determine how well wind energy integration has reduced the generation costs and emissions. Two case studies are performed in this work. For Case 1, the total generation costs and emissions using MOPSO are less, with a difference of $42.763, while MOALO has lower emissions, with a difference of 157.337 tons. For Case 2, with the implementation of wind energy, MOPSO has lower total generation costs, with a difference of $51.678, and lower emissions, with a difference of 459.446 tons.

Dynamic Cooling – A concept of time-sensitive thermal regulation to cut cooling energy demand in office buildings

This paper focuses on how the existing sealed office typology can adapt to a warming world while minimizing the high energy demand for space cooling, through Dynamic Cooling, by adjusting the cooling temperature profile towards the average human circadian rhythm to mitigate heat stress during times of heightened thermal sensitivity. This concept is explored through a study in which we compared the effects of Dynamic Cooling, constant cooling, and no cooling on the core body temperature of 21 participants as well as a thermal dynamic simulation of 270 office space configurations under the same cooling profiles, comparing the total energy demand, peak loads, and overall hours of discomfort. The results of the study suggest the different conditioning profiles have no direct impact on the overall change in core body temperature throughout the day (difference < 0.04 K) but do influence the overall range throughout (from 0.37 K under constant cooling to 0.53 K under Dynamic Cooling). Additionally, there is no clear correlation between elevated indoor temperatures and CBT during the morning hours, questioning the necessity and effectiveness of pre-lunch cooling. This is underlined by the simulation findings, which reveal a non-linear relationship between savings in cooling demand and a decline in thermal comfort, with a 51.3 % reduction in ideal cooling energy demand and a 25.2 %–36 % increase of Over-Temperature-Kelvin-Hours, from a steady state cooling profile to a profile of Dynamic Cooling. The results of the study and simulation challenge traditional cooling standards and highlight the need for a balance between minimizing thermal discomfort and the necessary cooling energy demand.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

The concept of autonomous hydrogen energy complex: Adaptation of large nuclear power units to uneven energy consumption schedules

Evaluation of flexible operation of nuclear power plants (NPP) during load reduction hours shows inefficiency of the process with account for high capital investments in NPPs at relatively low fuel costs. The concept of producing hydrogen fuel at NPPs during the hours of low power demand in the energy grid will be an effective solution to the problem. Generating electricity through utilization of electrolysis hydrogen and oxygen production provides a possibility to significantly extend the flexibility range for power released by the NPP in the energy grid under a constant reactor capacity factor. Utilization of a low-power steam turbine can be a good solution to address the challenges of electricity production from hydrogen and oxygen. This will ensure high reliability of the main NPP steam turbine. In addition, in their previous works based on the experimental data relating the operation of NPPs, the authors showed that a low-power steam turbine and residual energy release from the reactor can be used for NPP auxiliary needs backup in case of a complete blackout. Thus, increased safety of NPPs will be ensured by installing an autonomous hydrogen energy complex. Moreover, this can reduce the risk of an accident when using both the newly developed and existing safety systems. This work presents an analysis of the current state of hydrogen production at NPPs, including the research results and rationale for efficiency of the developed energy complex. The calculations showed that development of NPPs in combination with autonomous hydrogen energy complexes will increase economic attractiveness of newly designed NPPs and will increase the competitiveness of existing nuclear plants on electricity market, will also provide their survival in the energy sector in the long term.

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it

IntroductionA radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statementPhotovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. ObjectivesIn this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. MethodologyThe demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. ResultsThe analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. RecommendationNew storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.

Investments in transmission lines and storage units considering second-order stochastic dominance constraints

Increasing intermittent renewable generation to meet the climate goals entails a deep transformation of current power systems. The transmission system must adapt to ensure a rapid and flexible response to the changes in the energy flows. Flexibility can be provided by reinforcing the interconnection among all the market agents and/or by installing facilities with fast response to the changes. In this work, we study the investment problem of a central planner that seeks to expand the transmission network and install storage units considering decarbonizing measures. We propose a two-stage stochastic problem with uncertainty on the demand growth and including representative days to characterize the hourly demand and renewable power variability. To obtain better expansion strategies according to a limit on the carbon emissions, second-order stochastic dominance constraints are imposed. Numerical analyses based on the power system of the Canary island (Spain) are provided. The results show that to install storage units is key to efficiently integrate renewable generation. The investments in the transmission system are mostly in lower-voltage lines. The formulation with stochastic dominance constraints results in higher second-stage investments, allowing a better adaptation to the demand growth evolution.

Assessing the Effectiveness of an Innovative Thermal Energy Storage System Installed in a Building in a Moderate Continental Climatic Zone

In the present work, the operating results from an innovative, renewable, energy-based space-heating and domestic hot water (DHW) system are shown. The system used solar thermal energy as its primary source and was assisted by a shallow geothermal application in order to accommodate the space-heating and DHW needs of a domestic building in Austria. The system incorporated phase-change materials (PCMs) in specially designed containers to function as heat-storage modules and provide an energy storage capability for both the space-heating and DHW subsystems. This system was designed, implemented, and tested under real operating conditions in a building for a period of one year. The operating and energy results for the system are demonstrated in this work. The system was compared with a conventional one, and a reduction in the primary energy consumption equal to 84.3% was achieved. The maintenance and operating costs of the system were reduced by 79.7% compared to the conventional system, thus significantly contributing to the NZEB target of the building. The newly proposed system, although presenting an increased operating complexity, utilizes an innovative self-learning control system that manages all of its operations. The combination of a solar thermal energy source with thermal energy storage increases the use of renewable energy by extending the capacity of the system beyond the solar hours and using excess solar energy for space-heating needs. The thermal energy storage unit also increases the energy and economic efficiency of the geothermal heat pump by operating it during the hours of a reduced electricity tariff and using the stored energy during hours of a high electricity demand. The cost for the installation of such a system is higher than a conventional one, but due to the significantly decreased operating costs, the pay-back period was calculated to be 8.7 years.

Hydraulic and Utility Performance Evaluation of Ataye Town Water Distribution Network, Ethiopia: case for small towns in developing countries

Evaluating hydraulic and utility performance of water distribution network is a way to check the functionality degree of a system. Water CAD software was used in developing a model and evaluating the hydraulic performance of water distribution system of Ataye Town, Amhara Region, Ethiopia. Both steady state and extended period simulation analysis were carried out to determine hydraulic parameters (pressure and velocity). The model was calibrated using Darwin Calibrator and validity was checked by both correlation coefficient and scatter plot. The utility performance was also evaluated using international water association performance indicators. The simulated result for steady state analysis showed that 68.7% of the nodes operated within optimum adopted pressure (15-60 meter) and 93% of the distribution pipes had a velocity of &lt;0.6 m/s which was a minimum adopted velocity. For extended period simulation 34.3% during maximum demand time and 16.8% during minimum consumption hours had pressure &lt;15meters. 10.8% had negative pressure during maximum demand hours located at Sudan, Selama, and Zigba sefer and 14 nodes (8.4%) during low consumption time had a pressure greater than 60meter, which was a maximum adopted pressure. The areas of high pressure were located at around mosque, Worku Hotel and Hamus Gebaya. The distribution system performs within the adopted pressure at minimum demand time (74.7%) and maximum consumption hours (65.1%). 23.5% of the systems had a velocity of 0.6-2 m/s and the rest had a velocity less than 0.6m/s during high demand time. During low consumption hours, 100% of the system velocity was estimated to be &lt;0.6m/s. Based on the IWA performance indicators, water utility of Ataye Town was evaluated and had low, technical, financial, personnel, and environmental performances. Finally, the distribution system was modified and optimized by providing alternative connections with pressure reducing valves and changing pipe diameter to improve the hydraulic performance and reduce system disruption of the Town.&#x0D; Keywords: Extended period simulation; Hydraulic performance; IWA Utility performance; Steady state analysis; Water CAD

Holistic non-linear optimization of the layout, sizing, and operation of a district heating plant

A large fraction of district heating networks in Europe are still operating without thermal energy storage. While offering operational flexibility, the presence of energy storage complicates the problems of optimal sizing and control of the heat production plant. This necessitates a novel approach to system sizing based on non-linear programming in order to capture the system’s thermal response and inertia. Therefore, the present study focuses on such a typical plant in France and describes a holistic approach to optimize the capacities/parameters of system components, positions of the energy sources, as well as hourly flow rates. Such a simultaneous tuning of the system parameters and operating variables is essential to maximize the cost savings over the system's lifetime. The proposed framework here maintains the original non-linear form of the objective function and the constraints for more accuracy while allowing precise modeling of the thermally stratified storage tank by decoupling its calculations at a lower simulation level. Hence, the computation time in MATLAB® R2020A is mostly below 20 min on a typical personal computer. The lowest levelized cost of heat (57.259 EUR/MWh) is achieved when placing the gas boiler on a parallel line between the biomass boiler and the storage tank while sizing the two boilers using fractions of 0.33 and 0.73 of the maximum hourly demand, respectively, as well as a storage volume of 318.9 m3. Such relatively small storage reduces the lifecycle costs by a maximum of 1.34 % while reducing the specific carbon emissions by 17.33 %. Besides, it dampens the fluctuations in supply flow rates, minimizes unnecessary flow recirculation, and ensures a stable heat supply. Overall, thermal storage has a reasonable positive impact from an economic perspective, but a substantial impact on operational flexibility and mitigated carbon emissions.

Hedging Effectiveness on the MISO Exchange

The purpose of this paper is to evaluate the effectiveness of four commonly used hedging techniques on wholesale electricity prices within the Midcontinent Independent System Operator (MISO) energy market. This analysis provides and in-depth assessment of hedging performance on one of the largest power markets in North America. In a break from extant literature, hourly time series are used to create hedge portfolios for each of the regional hubs within the MISO footprint. Each risk management strategy is employed on 24 hourly time series for each of the eight regional hubs within the MISO footprint. This results in a total of 768 hedged portfolios across 192 hourly time series. While the naïve approach tends to outperform the others in terms of variance reduction, none of the strategies examined meet the generally accepted standard for a highly effective hedge. In several instances, the hedged portfolio contains a higher amount of risk than the unhedged position. This may be explained by the unique characteristics of wholesale electricity and hedging pressure, particularly during peak demand hours.

Capacity Credit of Solar PV Projects – Oman’s Main Interconnected System Case Study

Due to high growth in electricity demand, it is important to add more dispatchablegeneration capacity in power systems to maintain demand-supply balance in real time. In many power systems, fossil fuel-based units such as gas or coal power plants are used to maintain high-reliability standards during peak demand hours. Although intermittent renewable energy generation facilities such as solar PV systems are used as a fuel saver, they can offset part of the load during peak hours. Capacity credit measures the contribution of a power plant to reliably meet demand. Estimating capacity credit of solar PV systems is important to ensure the adequacy and security of electricity supply. In this article, the capacity credit of solar PV power plants in the Main Interconnected System (MIS) of Oman is modelled. Oman’s MIS considered in this study has 6,372 MW gas-based generation capacity, a negligible solar PV capacity, and 5,712 MW peak load. The results revealed that the capacity credit of solar PV plants is high at low solar PV penetration levels. However, it decreases drastically as solar PV capacity increases. For example, the first 500MW plant has a capacity credit of about 120 MW. When the cumulative installed solar PV capacity is 2000MW, the next 500MW plant has a capacity credit of only 26MW.