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Ensuring energy security and controlling the share of energy in climate change are the two main challenges of the energy sector in the path of a sustainable future. This study has designed the dynamic model of Iran’s electrical energy supply system based on the water-food-energy-climate change nexus in order to identify sustainable policies for the supply of electrical energy resources and adaptation to Iran’s climate change process.

For this purpose, first, the system dynamics model was designed with the participation of policymakers and the supply and demand data of water-food-energy resources and the trend of climate change and economic growth in Iran. After validation, the model is simulated in the 30-year horizon (2020–2050), and according to the results of the Monte Carlo sensitivity analysis, Iran’s electric energy supply policies are based on four strategies, including (1) Electric energy supply based on electric energy supply management from non-renewable sources, (2) Development of electrical energy supply based on management of energy supply from renewable sources, (3) Electrical energy supply based on electrical energy demand management and (4) Electrical energy supply based on adaptation to climate changes. By identifying and applying the policies of each strategy separately, the model was tested and the results were compared.

Based on the implementation of the combination of selected policies in the model, the following policies have been proposed: 16% development of nuclear power plants, 18% reduction in the ratio of production of gas power plants to the total production of non-renewable power plants, and an increase in the production of combined cycle power plants through conversion of gas to combined cycle, energy aggregation and development of heat recovery systems in industrial units by 32%, a decrease of 5% Energy transmission and distribution losses, per capita reduction of energy consumption from 0.926 to the global average of 0.182 (MW) per year, management of water demand in the food sector by increasing irrigation efficiency to about 85%, a 27% increase in the area of land under the irrigation network, and reducing losses Food amounting to the global average of 0.9 m tons per year.

The proposed model is an application of system dynamics in the field of policymaking to ensure the security of electrical energy resources, taking into account the water-food-energy-climate changes nexus. The model is a valuable tool for policymakers in planning the sustainable management of resources in the path of adapting to climate change.

Nuclear power is being promoted by a segment of the environmental community as an acceptable energy source to fight man-made climate change because it does not emit greenhouse gases. Missing in the literature is a discussion and analysis of the impact of electricity deregulation on the ability of nuclear power to obtain the requisite debt and equity financing within deregulated electricity markets, and in turn, on the potential number of new nuclear power plants that could help fight global warming. The purpose of this paper is to provide timely and salient policy guidance for the efficient allocation of resources to reduce greenhouse gases based on a new model linking debt and equity financing with a change in power plant revenue risk.

A theoretical model is put forth that links the availability of debt and equity financing to the change in revenue risk created by electricity deregulation and then tests this model by performing a qualitative phenomenological analysis.

The analysis supports a conclusion that electricity deregulation has a negative effect on the ability to attract nuclear plant debt and equity financing. As such, nuclear power may not be a viable option to reduce greenhouse gases within deregulated markets.

This paper fills certain gaps in the literature by creating a theory-based model that links debt and equity financing with a change in power plant revenue risk, performing a qualitative phenomenological analysis that finds support for the negative relationship between electricity deregulation and an increase in power plant revenue risk and establishing that this increase in revenue risk affects some types of power plants such as nuclear power more than others.

Long-term contract is an important developing direction of China's coal industry coordination. This paper aims to discuss how to use contract for difference (CFD) to avoid risk and effectively increase the benefit of both coal and thermal power plants in the coal-electricity supply chain.

Based on prospect theory, this paper takes the risks and benefits of the coal and coal-fired power plants in the coal supply chain under CFD into balanced consideration to construct the contract coordination mechanism. In this mechanism, the coal demand in the coal supply chain equilibrium under centralized decision-making is regarded as the total annual volume of transactions needed to design the contract coordination mechanism and solve double marginalization. Then, based on prospect theory, in the construction of CFD, this paper takes the income of power and coal enterprises when they are in equilibrium under Stackelberg non-cooperative game as the reference point. In addition, considering that coal demand is a random variable, the CFD with a one-year trading session can be designed.

The research derives the coal price of the contract for difference, contract trading volume and its proportion of the total trading volume. A numerical example shows that the model above can be used to effectively avoid the risk of both coal and electricity sides.

To solve the conflict between coal enterprises and thermal power plants, let the coal-electricity supply chain be converted from non-cooperative game to cooperative game. Based on the prospect theory, this paper takes the income of the non-cooperative game of coal and thermal power plants as a reference point and considers how to design the coordination mechanism, the contract for difference, so as to make the two parties cooperate to solve the double marginal utility of the non-cooperative game in a chain supply. The main innovation of the work lies in the following: first, the coal demand when the coal-electrical supply chain is in balance under centralized decision-making is taken as the total annual trading volume needed to design the contract coordination mechanism and solve double marginalization. Second, based on prospect theory, in the construction of CFD, the benefits of coal-fired power plants and coal enterprises when both sides are in equilibrium under the Stackelberg non-cooperative game are taken as the reference points, and coal demand is taken as a random variable to design the CFD with a one-year transaction period. The price of coal that is not traded through CFD is calculated according to the daily market price. Third, this paper proposes the prospect M-V criterion of the risk-benefit equilibrium of both power and coal enterprises, which means that the risk-benefit equilibrium of both sides is the prospect variance effect of both sides relative to the reference point benefit divided by the prospect expectation effect.

The purpose of this paper is to analyse the performance of a wind electric generating power plant through the study of reliability measures. The enhancement of the performance of the wind power plant using various approaches is also an objective of this paper.

This paper describes two models of a wind electric generating power plant using the Markov process and supplementary variable technique and solved with the help of Laplace transformation. The first model has been analyzed without fault coverage and Gumbel-Hougaard family of copula, while the second model of the wind power plant employs fault coverage and Gumbel-Hougaard family of copula which are used to enhance the performance. The proposed methodology is then illustrated in detail considering numerical examples.

Numerous reliability characteristics such as availability, reliability and mean time to failure to examine the performance of the wind power plant have been investigated. Through the comparative study of both the models, the authors concluded that the plant can generate electricity over long periods of time by covering more and more detected faults, which is made possible with two types of repair facility.

In this work, the authors have developed a mathematical model based on a wind electric generating power plant. This work incorporates not only the component failures that stop or degrade the working of the plant but also deals with the catastrophic and repair strategy of the plant.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Concern for health, safety and environment (HSE) is increasing in many developing countries, especially in energy industries. Improving power plants efficiencies in terms of HSE issues requires considering these issues in performance assessment of power generation units. This study aims to discuss the use of data envelopment analysis methodology for the performance assessment of electrical power plants in Iran by considering HSE and conventional indicators.

Installed capacity, fuel consumption, labor cost, internal power, forced outage hours, operating hours and total power generation along with HSE indices are taken into consideration for determining the efficiency of 20 electric power plants or decision-making units (DMUs). Moreover, DMUs are ranked based on their relative efficiency scores.

Results show that HSE factors are significant in performance assessment of the power plants studied in this research, and among HSE factors, health has the most powerful impact on the efficiency of the power plants.

The approach of this study could be used for continuous improvement of combined HSE and conventional factors. It would also help managers to have better comprehension of key shaping factors in terms of HSE.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

This study examines whether the 38 electric utility firms owning the 110 plants targeted by the 1990 Clean Air Act (CAA) made adequate pollution disclosures to inform the stakeholders whether they met the pollution emission requirements of the Act by the start of its first phase. First, it evaluates pollution emissions of the targeted plans at the start of the first phase of the Act, i.e. 1995. Then, it evaluates whether pollution disclosures of these firms improved leading up to the first phase of the Act. This evaluation is done by comparing pollution disclosures for the start of the first phase, i.e. 1995, with the year the CAA was enacted, i.e. 1990. Pollution emission data are obtained from the Department of Energy and from the Environmental Protection Agency (EPA), and pollution disclosure data for 1989, 1990 and 1995 are obtained from the annual reports and 10Ks. A specifically designed content analysis technique is used to categorize pollution disclosures.

The pollution emissions results indicate that 1995 emissions are significantly lower than 1990 emissions. On an individual plant basis, the results, however, indicated that some plants reduced emissions while others used the permit system. The pollution disclosures results indicate that the 1995 pollution disclosure are comparatively lower than 1990 disclosures. The reason for high disclosures for 1990 could have been to protect the firms against potential legal cases if the requirements were not met. Once the fears of legal actions subsided, pollution disclosures were probably reduced. Lack of consistency and adequacy in pollution disclosures, however, make it difficult for stakeholders to properly evaluate their future risks.

This paper considers the use and application of the “grey system” modelling methodology for the prediction and characterisation of boiler water R‐values in electric power generating plants. It has been confirmed that the GM (1,1) model can pinpoint precisely the properties of the water chemistry cycle. It is considered that the grey system methodology can strengthen significantly the effectiveness of system diagnosis, thereby enabling the development and application of automated water chemistry management systems in power plants.

To foster the grid integration of both electric vehicles (EV) and renewable generators, the purpose of this paper is to investigate the possible synergies between these players so as to jointly improve the production predictability while ensuring a green mobility. It is here achieved by the mean of a grid commitment over the overall power produced by a collaborative system which here gathers a photovoltaic (PV) plant with an EV fleet. The scope of the present contribution is to investigate the conditions to make the most of such an association, mainly regarding to the management strategies and optimal sizing, taking into account forecast errors on PV production.

To evaluate the collaboration added value, several concerns are aggregated into a primary energy criterion: the commitment compliance, the power spillage, the vehicle charging, the user mobility and the battery aging. Variations of these costs are computed over a range of EV fleet size. Moreover, the influence of the charging strategy is specifically investigated throughout the comparison of three managements: a simple rule of thumb, a perfect knowledge deterministic case and a charging strategy computed by stochastic dynamic programming. The latter is based on an original modeling of the production forecast error. This methodology is carried out to assess the collaboration added value for two operators’ points of view: a virtual power plant (VPP) and a balance responsible party (BRP).

From the perspective of a BRP, the added value of PV-EV collaboration for the energy system has been evidenced in any situation even when the charging strategy is very simple. On the other hand, for the case of a VPP operator, the coupling between the optimal sizing and the management strategy is highlighted.

A co-optimization of the sizing and the management of a PV-EV collaborative system is introduced and the influence of the management strategy on the collaboration added value has been investigated. This gave rise to the presentation and implementation of an original modeling tool of the PV production forecast error. Finally, to widen the scope of application, two different business models have been tackled and compared.