Electricity Supply Chain Research Articles

A game theoretic approach for time-of-use pricing with considering renewable portfolio standard effects and investment in energy storage technologies under government interventions

To remain in the competitive market, power companies, in addition to price competition, need to develop technologies to improve the reliability of electricity supply and achieve sustainable development goals. This paper explores energy pricing in an electricity supply chain consisting of renewable and conventional electricity producers and a distribution grid, focusing on improving supply reliability and achieving sustainable development goals. The study employs game theory to model government intervention in the electricity market through subsidization and Renewable Portfolio Standard (RPS) strategies. Three investment scenarios for improving reliability are considered, with producers determining wholesale prices and the distribution grid setting prices for low-and-high-load periods. The renewable producer and the distribution grid manage the reliability level by investing in energy storage technologies in the first and second scenarios, respectively, while it is ignored in the third scenario. The results reveal that optimal subsidy and RPS rates depend on the government's priorities regarding environmental effects, government revenue, and social welfare. Scenario two, is found to be the most favorable for improving supply reliability and achieving social welfare and environmental goals. Additionally, prioritizing social welfare maximization policy is found to be advantageous for producers' profit, the distribution grid, and supply reliability improvement.

A study of licensing strategies for energy storage technologies in the renewable electricity supply chain under government subsidies

With the rapid spread of renewable electricity, the licensing of energy storage technology has become an important way for technologically backward electricity suppliers to improve their competitiveness in the electricity market. However, there have been few studies that have investigated the influence of government policy on the selection of licensing strategies for energy storage technologies by renewable electricity suppliers. Consequently, we constructed a game model involving two competing renewable electricity suppliers, the grid company, and the government. One of them is a technologically advanced electricity supplier, and the other is a technologically backward one. The model considers government subsidies and compares three licensing strategies: technology monopolies (TM), unit-production authorization (UA), and fixed-fee authorization (FA). Based on numerical analysis, this paper compares wholesale electricity prices, grid orders and grid and government profits under different licensing strategies. Next, the choice of the optimal licensing strategy among electricity suppliers and the preferences of the grid and the government for different licensing strategies are analyzed. The results show that wholesale electricity prices increase and grid orders decrease under the UA strategy compared to the TM strategy, while the opposite is true under the FA strategy. The FA strategy is preferred between electricity suppliers when the cost of generation from technologically advanced suppliers exceeds twice the cost of generation from technologically inferior suppliers. Otherwise, the UA strategy is more appropriate. Moreover, the grid and government preferred the FA strategy to the TM or UA strategy, with profits rising 89% and 26% for the grid and government respectively under this strategy. Lastly, the grid is more profitable under the UA strategy than under the TM strategy only if the level of electricity reliability and grid cost are higher.

Pricing and green investment strategies for electric vehicle supply chain in a competitive market under different channel leadership

Pricing and green investment strategies for electric vehicle supply chain in a competitive market under different channel leadership

Optimal Decisions of Electric Vehicle Closed-Loop Supply Chain under Government Subsidy and Varied Consumers’ Green Awareness

The emerging operating mode of new automobile forces in the context of China brings up new problems for the development of the EV CLSC, which are the market position shift inside the loop and government policy and consumer awareness of changes outside it. Aiming at promoting the development of the EV CLSC, this study integrates the influence of internal and external factors, analyzes their joint impact on the performance of the EV CLSC, and explores the optimal CLSC structure. Specifically, this study develops five game theory models considering different CLSC structures and consumers’ green awareness under government subsidy and varied channel leaderships. Combining theoretical analysis with numerical simulation, the study reveals the performance of the EV CLSC and indicates the optimal CLSC decisions for different players. The result suggests that an EV manufacturer should develop an EV CLSC by itself considering the elimination of double marginalization. When a third-party player is incorporated, the EV manufacturer should undertake the collection effort by itself to optimize the benefit for both the EV manufacturer and the third-party player, especially considering the increasing green awareness of consumers. And a lift of each player’s channel power would always be a wise choice for each other. For the government which has the goals of promoting both the EV industry and the end-of-life treatment of obsolete EVs, a series of trade-offs including the CLSC structure and channel leadership should be considered prudently. This study contributes to the comprehensive understanding of the optimal decisions of the EV CLSC, and will benefit the development of the EV CLSC.

Pakistan’s Energy Dilemma and Its Consequences on Economic Growth

<p style="text-align:justify">Technological advancements in the last few decades have created energy and cost-efficient power plants, but it is not a prominent feature in the electricity supply-chain. While many long-term and short-term energy alternatives are available, Pakistan still has a significant number of people who do not have access to electricity; as there are many areas that are neither connected to the national grid nor the provincial grids. This paper aims to find the reasons behind the energy crisis in Pakistan, keeping Islamabad as the focus of study, and to find the effect of use of renewable resources on the reduction of shortage of electricity. We will be focusing on the facts that how energy crisis has been affecting the economic growth of the country.  Our data collection will be based on primary sources and secondary sources along with which we will be conducting pilot study to check the feasibility of using the solar energy as an alternative form of electricity.</p>

Fuzzy robust flexible programming with [formula omitted] measure for electric sustainable supply chain

Electricity as a source of energy, is one of the most essential part in our life. In recent time, it is noted that the temperature of earth increases in compare to the previous years. It is caused due to burning of the conventional energy sources such as gas, oil, and coal from thermal power plant and other sources, which brings about global warming. Bases on this fact, we propose a multi-objective mixed-integer programming model to design an electric sustainable supply chain network by considering resiliency and responsiveness. In order to formulate the environmental issues, a power-to-gas (P2G) technology is considered as an environment-friendly system. The presence of P2G can reduce the carbon level and it has some major advantages, i.e., it collects carbon dioxide (CO2) from thermal power plant and then converts CO2 to hydrogen (H2) with the help of water (H2O) and methane (CH4) gas. The drawbacks in addressing the uncertainty of the parameters are overcome by proposing a novel fuzzy robust flexible programming approach based on Me measure. Thereafter, a new approach namely, multi-choice conic goal programming with utility function is introduced to solve the proposed multi-objective model. Damodar Valley Corporation (DVC) in India is considered as a case study about electric network that envisages the sustainable issues of an electric supply chain. Finally, experimental results show that one bio-fuel plant, one photovoltaic plant, one air turbine plant, and two mixed-cycle power plants are build at the end of the scheduled period.

Discovering themes and trends in electricity supply chain area research

Discovering themes and trends in electricity supply chain area research

Long‐term service agreement in electricity supply chain with renewable energy penetration

We study a long‐term service agreement (LTSA) between an original equipment manufacturer (OEM) of a conventional power generator and a utility firm in the electricity supply chain. The OEM offers the LTSA to the utility firm, which specifies the service fee and the maintenance interval. The utility firm dynamically chooses among different resources (conventional, renewable, or emergency) to meet energy demand. An LTSA contracts on a generator's long‐term production schedule (i.e., the usage time and number of starts). We first characterize that the conventional generator's optimal operating mode (i.e., on or off) follows a two‐threshold policy, which shows a hysteresis phenomenon. Then, the optimal production from different resources can be obtained by minimizing the utility firm's cost of the current period. We prove that the OEM's optimal LTSA design on the maintenance interval and service fee can be solved sequentially, and capture the trade‐off between the service margin and the usage of conventional generator. Our numerical results show that the utility firm starts the conventional generator less often as the maintenance service fee of a start increases and the minimum conventional output decreases. The OEM has incentives to reduce the minimum output to make the conventional generator more flexible, but such improvement may not benefit the utility firm. Finally, increasing renewable penetration leads to more frequent cycling of the conventional generator and less conventional output. With more renewable generation and less conventional and emergency generation, higher renewable penetration reduces the utility firm's cost, the OEM's profit, and the total emission.

The behavioural evolution of the smart electric vehicle battery reverse supply chain under government supervision

PurposeRecycling and reuse of the electric vehicle (EV) batteries are ways to extend their limited lives. If batteries can be traced from production to recycling, it is beneficial for battery recycling and reuse. Using blockchain technology to build a smart EV battery reverse supply chain can solve the difficulties of lack of trust and data. The purpose of this study is to discuss the behavioural evolution of a smart EV battery reverse supply chain under government supervision.Design/methodology/approachThis study adopts evolutionary game theory to examine the decision-making behaviours of the government, EV manufacturers with recycled used batteries and third-party EV battery recyclers lacking professional recycling qualification.FindingsOn the smart reverse supply chain integrated by blockchain technology, a cooperative recycling strategy of the third-party EV battery recycler is the optimal choice when the government tends to actively regulate. The probability of the EV manufacturer choosing the blockchain adoption strategy exceeds (below) the threshold, and the government prefers negative (positive) supervision. According to numerical analysis, in the mature stage in the EV battery recycling industry, when the investment cost of applying blockchain is high, EV manufacturers' willingness to apply blockchain slows down, the government accelerates adopting a negative supervision strategy and third-party EV battery recyclers prefer cooperative recycling.Practical implicationsThe results of this study provide opinions on the strength of government supervision and the conditions under which EV manufacturers and third-party EV battery recyclers should apply blockchain and cooperate. On the other hand, this study provides theoretical analysis for promoting the application of blockchain technology in smart reverse supply chain.Originality/valueCompared with previous research, this study reveals the relevance of government supervision, blockchain application and cooperation strategy in smart EV battery reverse supply chain. In the initial stage, even if the subsidy (subsidy reduction rate) and penalty are high and the penalty reduction rate is low, the EV manufacturer should rather give up the application of blockchain technology. In the middle stage in the EV battery recycling industry, the government can set a lower subsidy (subsidy reduction rate) combined with a penalty or a higher penalty (penalty reduction rate) combined with a subsidy to supervise it. The third-party EV battery recycler is advised to cooperate with the EV manufacturer when the subsidy is low or the penalty is high.

Hybrid Power Generation Supply Chain Financing and Purchasing Strategies with Option Hedging against Disruption

Environmental pollution has stimulated cleaner and sustainable energy (CSE) resource consumption which fuels low-carbon electric-power growth. This consumption is particularly affected by two factors. One of them is the power grid’s capital size in the electricity supply chain (ESC) and the other is the carbon emission reduction level elasticity of electricity consumption. Further, the financial strategy directly exerts quantifiable influence on one of the factors of capital size, while the procurement strategy plays a role in another factor. However, the impact of strategic behavior on all participants in the ESC remains unexplored. Thus, we construct a game model for the ESC consisting of two heterogeneous power plants and a strategic power grid with financial constraints, in order to analyze the purchasing and financing strategies for low-carbon electric-power consumption and examine the profit scenario from the credit and option hedging. We find that for the power grid, high self-owned funds level or affluent credit line encourages procurement. And when considering the power grid funds are uniformly distributed on or are constant, this funds property variation surely imposes influence on the wind electricity purchases. We find price elasticity of demand shows monotonic on the purchase when the power grid funds are constant but not necessarily when funds are uniformly distributed. The two power plants pursue the most favorable scenario for profit-maximizing by means of credit interest rate. Whether the power plants increase the financial credit interest is contingent upon option portfolios, the grid’s fund property, and wind yield conditions. We also find whether the relations between the two power plants are competitive or complementary depending on the wind yield rate. Numerical study shows that when the power grid funds are uniformly distributed, executing the double option seems to be the most profitable choice for the power grid and the traditional energy power plant, whereas with the uniform distribution of the grid’s funds, executing the call option but abandoning put is the most profitable to the traditional energy power plant. Moreover, under the grid’s fund of uniform distribution, it can both motivate power user consumption for clean energy generation and expand the power grid’s capital size.

Life Cycle Assessment of Linear Alkylbenzene Sulfonate Production: An Adaptation to the Brazilian Context

Surfactants represent a class of compounds used in large-scale household sanitizers, the Linear Alkylbenzene Sulfonate being the most used. However, there is concern about these compounds’ environmental aspects and impacts. This work aims to analyze the life cycle inventory of the production of Linear Alkylbenzene Sulfonate from cradle to gate for the Brazilian context aiming to propose improvements regarding cumulative energy demand and global warming potential. The total energy demand of the base case scenario was 59 MJ-Eq and a carbon footprint of 1.71 kg CO2-eq. A scenario considering the switch in heat and electricity supply to renewable sources was proposed, and a global reduction of Global Warming Potential in 100 years of 15.73% was realized, reducing the carbon footprint of the heat supply chain by 73.07% and the electricity supply chain by 98.90%.

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

AbstractDemand forecasting is a key parameter to achieve optimal supply chain management at different levels. The basic metals industry is one of the most energy‐intensive industries in the electricity supply chain. The impossibility of large‐scale energy storage, reservation constraints, and its high costs, limitations on electricity transmission lines capacity, real‐time response to high priority strategic demands, and variety of energy rates at different hours and seasons are issues that challenge the electricity supply chain. A hybrid approach is presented in this paper to improve the accuracy of long‐term demand forecasting in the electricity supply chain. The proposed approach uses wavelet decomposition and long short‐term memory (LSTM) neural networks to produce new predictors for demand forecasting in case of defective data. The data used in this study consists of the recorded hourly demand of Espidan Iron Stone (EIS) company and data of Mobarakeh Steel (MS) company in Isfahan Province. The understudy time series includes a lot of interruptions due to non‐production of the factory or power outages and spikes which cause uncertainty that makes the forecasting more difficult in comparison with continuous time series. Multiple machine learning models including decision trees (DTs), boosted and bagged trees (BGTs), support vector regression (SVR) models, extreme learning machines (ELMs), and a LSTM neural network model are employed to evaluate the effectiveness of the proposed approach in this paper. A comparison of the results obtained using the hybrid proposed approach and training machine learning (ML) methods shows a notable reduction in forecasting error.

Capabilities Analysis of Electricity Energy Conservation and Carbon Emissions Reduction in Multi-Level Battery Electric Passenger Vehicle in China

The battery electric passenger vehicle (BEPV) has the potential to conserve electric energy and reduce carbon emissions, making it an effective tool for achieving low-carbon development in the road transport industry by replacing the internal combustion engine vehicle (ICEV). Several factors, such as comprehensive electricity power generation efficiency, proportion of thermal power, vehicle technical performance, regional mileage credibility and low temperature, affect the BEPV’s electricity energy consumption and carbon emissions. In this study, an electricity conservation index model and a carbon emission reduction index model for multilevel BEPVs are established to evaluate their capabilities of electricity energy conservation and carbon emissions reduction, considering the electricity supply chain, including the generation and transmission of electricity. The research shows that the electricity energy conservation ability of BEPVs is not outstanding, but their carbon emissions reduction ability is strong. When the composition of energy for electricity generation is transformed from 2025 to 2035, with a 10% increase in comprehensive electricity generation efficiency, all levels of BEPVs show fruitful electricity energy conservation ability. When the proportion of thermal power decreases to 10%, the carbon emissions reduction is exponentially reduced to 1/25 to 1/30 of ICEV’s total carbon emissions. However, the regional mileage credibility weakens the BEPVs’ ability to save energy and reduce emissions in most Chinese provinces except for the southwest and the south regional provinces, where the regional mileage credibility parameter can increase the energy conservation and carbon emission reduction performance of A00+A0 level BEPV. Low temperatures make BEPV models lose their electricity energy conservation advantage, but most models still have the characteristic of carbon emissions reduction. On this basis, the electricity energy consumption and carbon emissions of all BEPV models are higher than those of ICEVs when the low temperature endurance mileage accuracy is added.

Unleashing the circular economy in the electric vehicle battery supply chain: A case study on data sharing and blockchain potential

Electric vehicles are perceived as a key technology to make mobility more sustainable, leading to a sharp rise in electric battery production and use. However, electric vehicle batteries are only a sustainable solution if they support decreasing the total impact of the supply chain, which makes circularity initiatives a key element in this transition. Currently, this is limited by the data sharing among actors in the supply chain on critical information needed to support a circular economy approach. In this study we explore how data sharing and information technology support the development of circularity in electric vehicle supply chains and examine the role of blockchain technology to address the circularity needs of battery tracking and capability sharing. To allow a comprehensive analysis, we conduct a case study in the electric vehicle battery supply chain, including companies from multiple tiers to capture all relevant perspectives. The results show that data sharing supports extended value chain activities, evolving from a linear to a circular supply chain perspective. It also indicates that blockchain technology supports removing existing barriers for a circular economy by facilitating transparency and traceability, especially for second-life applications beyond the dominant players in this industry.

A Constrained South African Electricity Supply Chain - What Next?

Background: The South Africa’s reliance on fossil-fuels for electricity generation for nearly a century, has transformed the nation into a carbon economy, with a heavy carbon footprint, as over 80% of electricity generation and 40% of fossil-fuel production are from coal. The under-serviced aged coal-fired stations, hitherto fail to meet the generation capacity that has contributed power cuts for over a decade. This study explored the constraints of electricity supply chain that culminated into power outages since 2008 covering the aged coal-fired power stations and independent power producers (IPPs) that produces renewable sources of energy. Method: Qualitative research methodology and constructivism research paradigm were pursued, and theories of competitive advantage and sustainable development underpinned the study. The non-probability sampling process was used and interviews were recorded and transcribed. The emanating data was interpreted, coded and analysed via content analysis. Results: The results indicated urgent need for hiring maintenance expertise for the aged coal-fired power stations to minimise loadshedding, while also expediting renewable sources of energy development for the sustainability of electricity supply chain. Conclusion: The outcome of the study would align with the country’s chosen path of “Just Energy Transition” strategy, which embraces clean energy development from renewable sources.

Electric Vehicle Supply Chain Management: A Bibliometric and Systematic Review

With the advancement of electric mobility, critical materials that are used in the batteries and electronic equipment of electric vehicles tend to become scarce. This work aims to analyse the state-of-art of the electric vehicle supply chain through bibliometric and systematic reviews, using quantitative and qualitative indicators, to find critical points that represent risks to the supply chain and that should be focused on and to identify trends for further studies. The bibliometric review was carried out with the support of the Bibliometrix software. The systematic review was performed using the PRISMA method. The bibliometric analysis showed the importance of the costs associated with electric vehicles, as well as trends in studies related to sustainability and transparency in the supply chain. Although risk management in the supply chain appears to be relatively little studied when considering the authors’ keyword analysis, the systematic review showed that this process was the most studied topic. Even so, raw materials supply appeared as the topic most focused on, followed by an environmental impact assessment and cost analysis. There were also studies aiming to achieve competitiveness and analyse ecologically correct practices. The battery was the most studied component, but other components must be analysed in search of greater competitiveness in relation to conventional vehicles.

A Study of Integrated Model for Electricity Supply Chain Considering Renewable Energy Fraction and Demand Variation

This paper aims to derive integrated model for electricity supply chain considering renewable energy fraction and demand variation. Currently, attention is being paid to increasing the ratio of renewable energy generation in the electric power market which called Green Energy Coefficient (GEC). This paper aims to derive an integrated model of electricity supply chain considering renewable energy fraction and demand variation using multi-agent reinforcement learning. The subjects of the models are the consumption market model, the electricity market model, and the production market model. Also, the pricing process in electricity supply chain is analyzed using the proposed multi-agent simulations.

Research on Collaborative Management of Electric Power Material Supply Chain Based on In-Transit Material Management System in a Power Enterprise

Research on Collaborative Management of Electric Power Material Supply Chain Based on In-Transit Material Management System in a Power Enterprise

The establishment of electric vehicle supply chain and its resilience to supply chain interruptions

The establishment of electric vehicle supply chain and its resilience to supply chain interruptions

Investment strategy for renewable energy and electricity service quality under different power structures

<p style='text-indent:20px;'>This paper investigates and develops an electricity supply chain with an electricity generator and electricity retailer. The electricity generator invests in renewable energy and sells the electricity to the electricity retailer; the retailer then sells the electricity to consumers according to electricity service quality. Three models are considered: the vertical Nash game, the electricity-generator Stackelberg model, and the electricity-retailer Stackelberg model. Finally, the optimal solutions under different power-structure models are compared. Certain conclusions can be drawn from the findings: (1) the highest electricity service quality and renewable energy investment are obtained in the vertical Nash game model, the electricity generator obtains more profits in the electricity-generator Stackelberg model, and the electricity retailer obtains more profits in the electricity-retailer Stackelberg model. (2) The investment in renewable energy in the electricity-generator Stackelberg model is less than that in the electricity-retailer Stackelberg model, and the investment in electricity service quality in the electricity-retailer Stackelberg model is less than that in the electricity-generator Stackelberg model (under certain conditions). (3) Both increasing the electricity service quality sensitivity and renewable energy sensitivity could increase the optimal electricity price, electricity demand, electricity service quality, renewable energy investment, and supply-chain enterprises' profit.</p>