Showing papers on "Base load power plant published in 2021"

Combined Impact of Demand Response Aggregators and Carbon Taxation on Emissions Reduction in Electric Power Systems

Abstract: An emission rate-based carbon tax is applied to fossil-fueled generators with a demand response approach called Smart Grid resource allocation (SGRA). The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid. We illustrate this using the Roy Billinton test system and the results show potential for significant reductions in fossil fuel-based generation and CO2 emissions.

Energy Trading in Local Electricity Market With Renewables—A Contract Theoretic Approach

Abstract: Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.

Life Cycle Assessment (LCA) of a Concentrating Solar Power (CSP) Plant in Tower Configuration with and without Thermal Energy Storage (TES)

Abstract: Despite the big deployment of concentrating solar power (CSP) plants, their environmental evaluation is still a pending issue. In this paper, a detailed life cycle assessment (LCA) of a CSP tower plant with molten salts storage in a baseload configuration is carried out and compared with a reference CSP plant without storage. Results show that the plant with storage has a lower environmental impact due to the lower operational impact. The dependence on grid electricity in a CSP tower plant without storage increases its operation stage impact. The impact of the manufacturing and disposal stage is similar in both plants. When analyzed in detail, the solar field system and the thermal energy storage (TES) and heat transfer fluid (HTF) systems are the ones with higher impact. Within the storage system, the molten salts are those with higher impact. Therefore, in this study the impact of the origin of the salts is evaluated, showing that when the salts come from mines their impact is lower than when they are synthetized. Results show that storage is a key element for CSP plants not only to ensure dispatchability but also to reduce their environmental impact.

Monte Carlo simulation of electric vehicle loads respect to return home from work and impacts to the low voltage side of distribution network

Abstract: Usage of electrical vehicles (EV) is increasing at high rate due to their great benefits to the community well-being. However, EVs have considerable impacts to electrical power networks and especially to the low voltage side of the distribution network. In order to determine the impacts of EVs accurately, uncertain behaviors of drivers were modeled using Monte Carlo simulations. This method is proven to be a robust tool for the evaluation of stochastic processes and getting deterministic results out of it. Furthermore, real-world traffic pattern data were used to model drivers’ behaviors. Return home time of EVs was used as a charging start time, and average commute distance of drivers was used to determine the charging duration. Also, residential area was taken as a pilot network. Hourly basis transformer loading data were obtained and used to realistically reflect the base load of the pilot network. Load flow analysis was performed for non-EV and with-EVs scenarios. The results of the analysis were represented in a probabilistic approach. Violations of results were investigated according to power quality limits. Consequently, impacts of the EV charging load to the low voltage side of distribution network were analyzed in terms of voltage drops, transformers’ loadings, power losses and voltage unbalance. This study showed that with a 50% penetration rate of EVs, the probability of voltage violation increases by approximately 25%.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Abstract: Due to the impact of occupants’ activities in buildings, the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term, which show seasonal variation and hourly variation, respectively. This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time. In order to solve this problem, this paper proposes two approaches for fitting and predicting the electricity demand of office buildings. The first proposed approach splits the electricity demand data into fixed time periods, containing working hours and non-working hours, to reduce the impact of occupants’ activities. After finding the most sensitive weather variable to non-working hour electricity demand, the building baseload and occupant activities can be predicted separately. The second proposed approach uses the artificial neural network (ANN) and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. In this approach, the power demand data is split into a narrower time range as no-occupancy hours, full-occupancy hours, and fuzzy hours between them, in which the occupancy rate is varying depending on the time and weather variables. The proposed approaches are verified by the real data from the University of Glasgow as a case study. The simulation results show that, compared with the traditional ANN method, both proposed approaches have less root-mean-square-error (RMSE) in predicting electricity demand. In addition, the proposed working and non-working hour based regression approach reduces the average RMSE by 35%, while the ANN with fuzzy hours based approach reduces the average RMSE by 42%, comparing with the traditional power demand prediction method. In addition, the second proposed approach can provide more information for building energy management, including the predicted baseload, peak load, and occupancy rate, without requiring additional building parameters.

The evolving role of geothermal energy for decarbonizing the United States

Abstract: Geothermal energy is often referred to as a niche technology that is too localized, too small or too expensive to make much of a difference in how renewable energy will be supplied in a fully decarbonized future. As a result, geothermal energy has been undervalued in terms of what it could provide to complement, rather than compete with, electricity generation from wind, solar photovoltaic, concentrating solar power and other renewables. Geothermal energy systems are fully dispatchable and can provide baseload or load-following electric power or heat suitable for a wide range of applications including supplying district heating for communities and cities, and heating and cooling of individual buildings. The focus of our study is on the potential of utilizing geothermal energy for providing heat for buildings and industry at lower temperatures, a substitute for the combustion of fossil fuels. Because heating represents about 20% (20 EJ per year) of the annual primary energy consumption in the U.S.—with most of it coming from burning natural gas, oil and/or propane in furnaces—deploying geothermal heating on a national scale could have a significant impact on lowering carbon emissions. In heating-dominated states in the U.S. Northern Tier, heating often is among the largest contributors to the state's carbon footprint. This review begins with a discussion of the motivation and rationale behind considering geothermal as a key low-carbon heating option for the U.S. The study summarizes the U.S. geothermal resource and describes the applications and main engineering components of using geothermal energy for heating and cooling, electric power generation, and co-generation using district heating, geothermal heat pumps, and power conversion with steam flashing and organic Rankine plants. Environmental benefits and impacts are described. An extensive discussion of geologic and thermal-hydraulic aspects of the subsurface is included in the review because of their critical role in determining reservoir designs at specific sites to ensure sufficient productivity that is both safe and economically viable. Models for estimating levelized costs of district heating are used to show how costs are affected by reservoir performance, infrastructure capital costs, and financial parameters. The review concludes with an assessment of technical subsurface issues associated with reservoir performance and the economic requirements for providing geothermal heating in district heating systems at a sufficient scale to have an impact on decarbonizing the U.S.

The role of fuel cells in port microgrids to support sustainable goods movement

Abstract: Facilities such as ports that are associated with goods movement face challenges in managing energy requirements including growing demands, maintaining economic competitiveness, increasing efficiencies of operation, and improving the resiliency, reliability, and security of the energy supply. Furthermore, ports face pressure to meet environmental goals including reducing the emissions of both pollutants and greenhouse gases and increasing the levels of renewable sources. Within this framework, many port operators are pursuing the development of microgrids supported by self-generation including fuel cell systems. Given the breadth of energy requirements of ports, the range of fuel cell applications is an attractive resource for stationary power generation, motive power and fuel generation, and backup/auxiliary power in concert with base load power. This work involves a review of the literature, from a technical perspective, to assess microgrids and fuel cell systems at ports including comparison with combustion-based power distributed generation sources. Additionally, novel simulations are presented to provide insight into economic and emission considerations associated with fuel cell deployment in critical facilities. Important distinctions of fuel cells for ports include flexibility of size and fuel, low to negligible emissions, capability to operate in grid-forming mode, and high electric-only efficiencies. While combined cooling, heating, and power improves performance and should be pursued, the mismatch in port electrical and thermal loads is a potential barrier and increases the importance of high electric-only efficiencies of fuel cells. Tri-generation systems have the potential to maximize benefits with the production of hydrogen along with electricity and, if needed, heat.

Dynamic Prospective Average and Marginal GHG Emission Factors—Scenario-Based Method for the German Power System until 2050

Abstract: Due to the continuous diurnal, seasonal, and annual changes in the German power supply, prospective dynamic emission factors are needed to determine greenhouse gas (GHG) emissions from hybrid and flexible electrification measures. For the calculation of average emission factors (AEF) and marginal emission factors (MEF), detailed electricity market data are required to represent electricity trading, energy storage, and the partial load behavior of the power plant park on a unit-by-unit, hourly basis. Using two normative scenarios up to 2050, different emission factors of electricity supply with regard to the degree of decarbonization of power production were developed in a linear optimization model through different GHG emission caps (Business-As-Usual, BAU: −74%; Climate-Action-Plan, CAP: −95%). The mean hourly German AEF drops to 182 gCO2eq/kWhel (2018: 468 gCO2eq/kWhel) in the BAU scenario by the year 2050 and even to 29 gCO2eq/kWhel in the CAP scenario with 3700 almost emission-free hours from power supply per year. The overall higher MEF decreases to 475 and 368 gCO2eq/kWhel, with a stricter emissions cap initially leading to a higher MEF through more gas-fired power plants providing base load. If the emission intensity of the imported electricity differs substantially and a storage factor is implemented, the AEF is significantly affected. Hence, it is not sufficient to use the share of RES in net electricity generation as an indicator of emission intensity. With these emission factors it is possible to calculate lifetime GHG emissions and determine operating times of sector coupling technologies to mitigate GHG emissions in a future flexible energy system. This is because it is decisive when lower-emission electricity can be used to replace fossil energy sources.

Stand-Alone Direct Current Power Network Based on Photovoltaics and Lithium-Ion Batteries for Reverse Osmosis Desalination Plant

Abstract: Plummeting reserves and increasing demand of freshwater resources have culminated into a global water crisis. Desalination is a potential solution to mitigate the freshwater shortage. However, the process of desalination is expensive and energy-intensive. Due to the water-energy-climate nexus, there is an urgent need to provide sustainable low-cost electrical power for desalination that has the lowest impact on climate and related ecosystem challenges. For a large-scale reverse osmosis desalination plant, we have proposed the design and analysis of a photovoltaics and battery-based stand-alone direct current power network. The design methodology focusses on appropriate sizing, optimum tilt and temperature compensation techniques based on 10 years of irradiation data for the Carlsbad Desalination Plant in California, USA. A decision-tree approach is employed for ensuring hourly load-generation balance. The power flow analysis evaluates self-sufficient generation even during cloud cover contingencies. The primary goal of the proposed system is to maximize the utilization of generated photovoltaic power and battery energy storage with minimal conversions and transmission losses. The direct current based topology includes high-voltage transmission, on-the-spot local inversion, situational awareness and cyber security features. Lastly, economic feasibility of the proposed system is carried out for a plant lifetime of 30 years. The variable effect of utility-scale battery storage costs for 16–18 h of operation is studied. Our results show that the proposed design will provide low electricity costs ranging from 3.79 to 6.43 ¢/kWh depending on the debt rate. Without employing the concept of baseload electric power, photovoltaics and battery-based direct current power networks for large-scale desalination plants can achieve tremendous energy savings and cost reduction with negligible carbon footprint, thereby providing affordable water for all.

Central versus wellhead power plants in geothermal grid electricity generation

Abstract: The long gestation period, high upfront costs and the risks in the development of central geothermal power plants are the main reasons for the slow rate of geothermal electricity growth and its contribution to the global electricity mix. The overall objective of this study was to make a comparison between central geothermal power plants and wellhead power plants in the delivery of geothermal electricity projects. The study showed that wellhead power plants are generally less efficient compared to central power plants because of higher specific steam consumption, but are financially attractive because of the quicker return on investment, early electricity generation and the lower financial risks. The study showed that permanent wellhead power plants are a better option for geothermal wells with too low or too high steam pressure compared to others in the steam field. Temporary use of wellhead power plants as opposed to their permanent use is preferred when only limited time is available between the commissioning of a wellhead plant and the commissioning of a central power plant in the same steam field. Technical, operational and environmental challenges, including higher specific steam consumption and lower efficiency than central power plants as well as absence of geothermal fluid reinjection system make wellhead plants less economical and less sustainable in resource use. It can thus be concluded that wellhead power plants can reduce the long wait to generate geothermal electricity and make an early return on investment for investors. Both central and wellhead power plants have relatively higher capacity factor than many other power plants and so can be used to supply base load electricity for the grid or off-grid power supply. This study is a review of the central and wellhead power plants and additionally provides policy guidelines in the execution of geothermal electricity projects either as central or wellhead power plants for grid electricity generation.

Demand-Driven Biogas Production from Anaerobic Digestion of Sewage Sludge: Application in Demonstration Scale

Abstract: The power system needs flexible electricity generators. Whilst electricity generation from anaerobic digestion (AD) of sewage sludge has traditionally been baseload, transforming the generation capacity into a modern flexible operator is an opportunity to further valorise the resource. This work aims to demonstrate that AD of sewage sludge can support flexible generation and be operated dynamically in a relevant operational environment, to promote full scale implementation. A demonstration scale plant (20 m3 conventional AD reactors) was used to test several feeding regimes designed to return a biogas production rate that matches the demand. Two demand profiles are defined, either by common corporate power purchase agreements or by the main balancing mechanism used by the grid operator in UK. Demand-driven biogas production is demonstrated in this relevant operational environment, and the flexibilisation performance is positive in all scenarios. The value of the biogas increases by up to 2%, which outperforms the results obtained at pilot scale. Additionally, an increase in biogas yield is observed. Whilst transitional imbalances are recorded, they last for few hours and the overall stability is not affected. In conclusion, these trials demonstrate demand-driven biogas production is a feasible operational solution and full-scale implementation is possible.

Ageing Evaluation of the Distribution Transformer under Varying Load due to Electric Vehicle Charging

Abstract: Electric vehicles are expected to offer many benefits over conventional vehicles, such as reduction of carbon emission and fuel costs. However, the adoption of electric vehicles would bring great challenges to the distribution transformer, as their charging behavior would make the transformer operate under the varying load and therefore submit to overload risk. Hence, the insulation aging which shortens the transformer lifetime would be accelerated. In this research, we study the impacts of electric vehicle charging on the aging of the distribution transformer with conventional and high-temperature insulations. The number of households, base load and charging habits of a district in Jiaxing city are collected, and then a Monte Carlo simulation model is proposed to evaluate the additional load demand due to the electric vehicle charging. Furthermore, the simulated load demand is input to a model, which is modified based on the algorithm stipulated in IEEE C57.91, to calculate the thermal rise and the aging of the distribution transformer. The results imply that the electric vehicle charging would exacerbate the swing of the load demand. Particularly, the lifetime of the conventional distribution transformer would be significantly reduced. In comparison, the high-temperature insulation could mitigate the negative impacts of the electric vehicle charging on the transformer ageing.

The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations

Abstract: Author(s): Millstein, D; Dobson, P; Jeong, S | Abstract: Geothermal power plants have typically been operated as baseload plants. The recent expansion of wind and solar power generation creates a potential opportunity to increase the value of geothermal generation through flexible operations. In recent years, California's wholesale electricity markets have exhibited frequent, short-term periods of negative pricing, indicating that additional flexibility would be valued in the market. Here, we examine local nodal hourly price records at all geothermal plants located in the western United States. We describe how the frequency and temporal characteristics of negative pricing episodes have changed over recent years. Based on these price series, we calculate the value of multiple strategies of flexible operations. Additionally, we use the estimates of future prices, developed through a capacity-expansion model and a dispatch model, to explore how the value of such flexible operations might change along with further penetration of variable renewable power sources. Based on the historical pricing records, we find that simple curtailment of operations during negative pricing episodes could increase the average energy value by 1-2 $/MWh and that allowing for increased production during limited high-priced hours, in addition to curtailment during negative priced hours, could potentially double the increase in value (up to 4 $/MWh). The forward-looking simulations indicate reduced values of flexibility from geothermal power despite higher penetrations of variable renewable energy. This result highlights the possibility that increasing flexibility options throughout the system may counteract the influence of increased variable renewable energy deployment.

Opportunities for the integration of existing biogas plants into the Austrian electricity market

Abstract: With the increased expansion of photovoltaics and wind power, the electricity grid, which until then had been supplied centrally and with reliable forecasts, was confronted with decentralised and fluctuating power generation capacities. In future, it will be necessary to make use of further control options to provide for secured and controllable green electricity capacities. This article examines the option of market-oriented electricity production from existing biogas plants in Austria. A targeted survey was conducted to assess the biogas plants’ possibilities and limits for flexible electricity production. To determine the economic efficiency of flexible electricity generation, a 500 kW biogas plant was modelled on the basis of the available data. If all Austrian biogas plants with a capacity of 500 kW were integrated into control power pools for provision of secondary control power, an approximate capacity of 35 MW would be available. The results show that an additional premium (compared to base load production) of up to 35.4 €/MWh is required to cover the necessary investment and to achieve economic operation. Nevertheless, transformation losses caused by over- or under-capacities of wind power and photovoltaics can be reduced by flexible electricity production and active management of biogas plants.

Energy flexible CHP-DHN systems: Unlocking the flexibility in a real plant

Abstract: The purpose of this paper is to identify and analyze the impact of flexibility enablers in cogeneration and district heating network (CHP-DHN) plants by means of a real case study located in central Italy. A wider definition of energy flexibility applicable to the entire energy supply chain (i.e. production, transport and usage) is used in this analysis. In particular the flexibility is intended as the capability of each part of the system to produce a variation in its load curve, while ensuring the required performance. In this sense energy efficiency technologies, the use of energy storage and advanced control techniques can be seen as flexibility enablers potentially available in each section of the energy system. The innovative contribution of this work is to propose flexibility strategies in compliance with the constraints imposed by both the managers and users. The study aims to show possible ways to activate flexibility services to be used with known instruments and to quantify their impact with a simulation-based approach. In particular, three different flexibility instruments are identified in different sections of the plant: (i) the use of a thermal energy storage (TES) in the generation side, (ii) the optimal management of the DHN supply temperature (energy distribution side) and (iii) the management of the thermostatically controlled loads (TCLs) of the final users (demand side) connected to the network. Through the implementation of simulation models calibrated with available measurements, the influence of these flexibility instruments on the energy/environmental performance is evaluated in comparison to the current configuration of the plant. Results confirm the great impact of the TES to increase the CHP working hours and, as a consequence, a primary energy saving increase is obtained in mid-season and in summer season. Whereas the optimal management of the water supply temperature in the DHN allows to obtain 1% fuel reduction in a typical winter week and 2% in a typical summer week. As far as the activation of the demand side flexibility is concerned, the effect of the management of TCLs on energy conservation is demonstrated: 1 °C reduction of the setpoint of all the residential users during a typical winter day produces a 7.3% reduction of the DHN thermal demand. However, its impact on the generation side (i.e. to reduce the electricity/thermal production of the CHP at specific times) is limited due to the characteristics of the considered CHP plant (the CHP engine is sized to cover only the thermal baseload and it scarcely affected by thermal demand variations). The analysis proposed helps to obtain valuable hints on unlocking the energy flexibility in CHP-DHN plants useful for a better management of such systems.

Renewable On-Site Power Generation for Manufacturing Companies—Technologies, Modeling, and Dimensioning

Abstract: The expansion of renewable energies and the concomitant compensatory measures, such as the expansion of the electricity grid, the installation of energy storage facilities, or the flexibilization of demand, lead to a more elaborated energy supply system. Furthermore, the technological development of small power plants has further progressed, and many novel technologies have achieved grid parity. For manufacturing companies, the integration of renewable generation plants at their own site therefore represents a promising strategy for being both technically independent of the electricity grid and autonomous of price policy decisions and volatile market prices. This paper outlines the existing decentralized, renewable power generation technologies, their energetic modeling, and a hybrid optimization methodology for their dimensioning that uses mixed integer linear programming (MILP) and linear programming (LP) problem formulation. Finally, the introduced dimensioning method is applied to an exemplary manufacturing company that is assumed to be in the central part of Germany and located in the metalworking sector. The company has an electricity demand of approximately 20,000 MWh/a. The optimization results in a maximum expansion of PV and the use of CHP to cover the base load leading to a promising energy cost reduction of almost 20%.

Evaluating an economic application of renewable generated hydrogen: A way forward for green economic performance and policy measures.

Abstract: Energy security and environmental measurements are incomplete without renewable energy; therefore, there is a dire need to explore new energy sources. Hence, this study aimed to measure the wind power potential to generate renewable hydrogen (H2), including its production and supply cost. This study used first-order engineering model and net present value to measure the levelized cost of wind-generated renewable hydrogen by using the data source of the Pakistan Meteorological Department and State Bank of Pakistan. Results showed that the use of surplus wind and renewable hydrogen energy for green economic production is suggested as an innovative project option for large-scale hydrogen use. The key annual running expenses for hydrogen are electricity and storage costs, which have a significant impact on the costs of renewable hydrogen. The results also indicated that the project can potentially cut carbon dioxide (CO2) pollution by 139 million metric tons and raise revenue for wind power plants by US$2998.52 million. The renewable electrolyzer plants avoided CO2 at a rate of US$24.9–36.9/ton under baseload service, relative to US$44.3/ton for the benchmark. However, in the more practical mid-load situation, these plants have significant benefits. Further, the wind-generated renewable hydrogen delivers 6–11% larger annual rate of return than the standard CO2 catch plant due to their capacity to remain running and supply hydrogen to the consumer through periods of plentiful wind and heat. Also, the measured levelized output cost of hydrogen (LCOH) was US$6.22/kgH2, and for the PEC system, it was US$8.43/kgH2. Finally, it is a mutually agreed consensus among environmental scientists that the integration of renewable energy is the way forward to increase energy security and environmental performance by ensuring uninterrupted clean and green energy. This application has the potential to address Pakistan’s urgent issues of large-scale surplus wind- and solar-generated energy, as well as rising energy demand.

Short-term stochastic movements of electricity prices and long-term investments in power generating technologies

Abstract: Modeling probability distributions for the long-term dynamics of electricity prices is of key importance to value long-term investments under uncertainty in the power sector, such as investments in new generating technologies Starting from accurate modeling of the short-term behavior of electricity prices, we derive long-term stationary probability distributions Then, investments in new baseload generating technologies, namely gas, coal and nuclear power, are discussed In order to compute the stochastic Net Present Value of investments in new generating technologies, the revenues from selling electricity in power markets as well as the costs which come from buying fuels at uncertain market prices must be evaluated over very long time horizons, ie, over the whole lifetime of the plants Starting from accurate short-term stochastic models of fuel prices in addition to electricity prices, we provide long-run probability distributions which are used to compute revenues and costs incurring during the whole lifetime of the plants Five sources of uncertainty are taken into account, namely electricity market prices, fossil fuel prices (natural gas and coal prices), nuclear fuel prices and $$\hbox {CO}_{\text{2 }}$$ prices Our evaluation model is calibrated on empirical data to account for both historical market prices and macroeconomic views about future trends of electricity and fuel prices The full probability density of the stochastic Net Present Value is thus determined for each generation technology considered in this study