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In the hybrid electricity market consisting of renewable and conventional energy, the generation output of renewable power is uncertain because of its intermittency, and the power market demand is also fluctuant. Meanwhile, there is fierce competition among power producers in the power supply market and retailers in the demand market after deregulation, which increases the difficulty of renewable energy power grid-connection. To promote grid-connection of renewable energy power in the hybrid electricity market, the authors construct different contract decision-making models in the “many-to-many” hybrid power supply chain to explore the pricing strategy of renewable energy power grid-connecting.

Considering the dual-uncertainty of renewable energy power output and electricity market demand, the authors construct different decision-making models of wholesale price contract and revenue-sharing contract to compare and optimize grid-connecting pricing, respectively, to maximize the profits of different participants in the hybrid power supply chain. Besides, the authors set different parameters in the models to explore the influence of competition intensity, government subsidies, etc. on power pricing. Then, a numerical simulation is carried out, they verify the existence of the equilibrium solutions satisfying the supply chain coordination, compare the differences of pricing contracts and further analyze the variation characteristics of optimal contract parameters and their interaction relations.

Revenue-sharing contract can increase the quantity of green power grid-connection and realize benefits Pareto improvement of all parties in hybrid power supply chain. The competition intensity both of power supply and demand market will have an impact on the sharing ratio, and the increase of competition intensity results in a reduction of power supply chain coordination pressure. The power contract price, spot price and selling price have all been reduced with the increase of the sharing ratio, and the price of renewable power is more sensitive to the ratio change. The sharing ratio shows a downward trend with the increase of government green power subsidies.

On the basis of expanding the definition of hybrid power market and the theory of newsvendor model, considering the dual-uncertainty of green power generation output and electricity market demand, this paper builds and compares different contract decision-making models to study the grid-connection pricing strategy of renewable energy power. And as an extension of supply chain structure types and management, the authors build a “many-to-many” power supply chain structure model and analyze the impact of competition intensity among power enterprises and the government subsidy on the power grid-connecting pricing.

In the hybrid electricity market, renewable energy power generator faces the uncertainty of power market demand and the randomness of the renewable energy generation output. In order to improve the grid-connected quantity of green power, the purpose of this paper is to design the pricing mechanism for renewable energy power generator with revenue-sharing contract in a two-stage “multi-single” electricity supply chain which contains a single dominant power retailer and two kinds of power suppliers providing different power energy species.

Considering the dual uncertainties of renewable energy power output and power market demand, the authors design the full-cooperative contract decision-making model, wholesale price contract decision-making model and revenue-sharing contract decision-making model to compare and optimize grid-connected pricing in order to maximize profit of different parties in power supply chain. Then, this paper performs a numerical simulation, discusses the existence of the equilibrium analytical solutions to satisfy the supply chain coordination conditions and analyzes the optimal contract parameters’ variation characteristics and their interaction relationship.

The authors find that the expected profits of the parties in the hybrid power supply chain are concave about their decision variables in each decision-making mode. The revenue-sharing contract can realize the Pareto improvement for all parties’ interest of the supply chain, and promote the grid-connected quantity of green power effectively. The grid-connected price will reduce with the increase of revenue-sharing ratio, and this impact will be greater on the renewable energy power. The greater the competition intensity in power supply side, the smaller the revenue-sharing ratio from power purchaser. And for the same rangeability of competition intensity, the revenue-sharing ratio reduction of thermal power is less than that of the green power. The more the government subsidizing green power supplier, the smaller the retailer sharing revenue to it.

Facing with the dual uncertainties of green power output and market demand and the competition of thermal power in hybrid electricity market, this study can provide a path to solve the problem of renewable energy power grid-connecting. The results can help green power become competitive in hybrid power market under loose regulations. And this paper suggests that the government subsidy policy should be more tactical in order to implement a revenue-sharing contract of the power supply chain.

This paper studies the renewable energy electricity grid-connected pricing under the uncertainty of power supply and market demand, and compares different contract decision-making strategies in order to achieve the power supply chain coordination. The paper also analyzes the competition between thermal power and renewable energy power in hybrid electricity market.

When the power generator faces uncertain and independent electricity spot price and renewable energy source supply, two different conditions need to be considered: the distributions of renewable energy source electricity and electricity spot price are independent or dependent. The purpose of this paper is to explore the capacity investment strategy under volatile electricity spot price when renewable energy penetration rate is low, taking into account these two conditions.

The authors design a capacity investment model under dual uncertainties and consider how to optimize the investment capacity in order to maximize profit under two different conditions.

The authors find that when renewable energy supply fluctuation is unrelated to spot electricity price fluctuation, the renewable energy power profitability is determined by the average cost of spot electricity price and equivalent cost. When renewable energy supply fluctuation is related to spot electricity price fluctuation, the renewable energy power profitability is determined by the market value and the construction and maintenance cost.

Faced with the conflict of the renewable energy supply, the authors need to understand how to plan the generation capacity with intermittent renewable sources. The result helps renewable energy become competitive in the electricity market under loose regulations.

The authors compare two capacity investment strategies that the renewable energy supply fluctuation is related and unrelated to spot electricity price.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

The purpose of this paper is to examine the impact of renewable energy on the power supply chain and to study whether the renewable generator or the power grid that purchases power from the power spot market is better when the actual generation of renewable energy is insufficient. The authors want to compare and analyze the different power supply chain operation modes and discuss the optimal mode selection for renewable energy generator and power grid in different situations.

This paper studies the grid-led price competition game in the power supply chain, in which the power grid as a leader decides the price of transmission and distribution, and generators determine the power grid price. The renewable energy power generator and the traditional energy power generator conduct a price competition game; on the other hand, the power grid and power generators conduct Stackelberg games. The authors analyze the power supply of single power generator and two power generators, respectively, and research on the situation that the renewable energy cannot be fully recharged when the actual power generation is insufficient.

The study finds that both renewable and traditional power grid prices decline as price sensitivity coefficient of demand and installed capacity of renewable energy generators increase. Power grid premium decreases as the price sensitivity coefficient of demand increases, but rises as the installed capacity of renewable energy generator increases. When there is a shortage of power, if the installed capacity of renewable energy is relatively small and price sensitivity coefficient of demand is relatively large, the grid purchases the power from power spot market and shares cost with renewable energy generators, leading to higher expected profits of the renewable energy generators. On the contrary, the renewable energy generators prefer to make up power shortage themselves. For the power grid, purchasing the power by the renewable energy generators when there is a power shortage can bring more utility to the power grid when the installed capacity of renewable energy is lower and the demand price sensitivity coefficient is higher. When the installed capacity of renewable energy is high and the price sensitivity coefficient of demand is moderate, or the installed capacity of renewable energy is moderate and the demand price sensitivity coefficient is high, a generator that simultaneously possesses two kinds of energy source will bring more utility to the power grid. If the installed capacity of renewable energy and the demand price sensitivity coefficient both are small or the installed capacity of renewable energy and the price sensitivity coefficient of demand both are large, the power grid prefers to purchase the power by itself when there is a power shortage.

The goal of our paper analysis is to explore the implications of the theoretical model and address the series of research questions regarding the impact of the renewable energy on the power supply chain. The results of this study have key implications for reality. This paper sheds light on the power supply chain operation mode selection, which can potentially be used for the renewable energy generators to choose their operating mode and can also help traditional energy generators and power grid enterprises maximize their utility. This paper also has some references for the government to formulate the corresponding renewable energy development policy.

This paper studies the power operation mode under the uncertainty of supply and demand, and compares the advantages and disadvantages of renewable energy generator that makes up the shortage or the power grid purchases the power from power spot market then shares cost with the renewable energy generator. This paper analyzes the power grid-led coordination problem in a power supply chain, compares and analyzes the price competition game model of single power generator and dual power generators, and compares the different risk preferences of power grid.

The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making behavior of players in a cassava-based bioethanol supply chain under random yield and demand environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply chain resiliency and performance.

The decision-making behavior under three models, namely, centralized model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing coefficients and randomly fluctuating cassava market price on the decision-making behavior and performances are provided.

Though the proposed risk-sharing model cannot achieve the supply chain performance in the centralized model, it does help to encourage the farmers and the company to increase the supply of cassava and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these improvements will be enlarged as the yield uncertainty risk is higher.

The findings will help decision makers in the bioethanol supply chain to understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the development of the bioethanol industry.

The proposed risk-sharing model incorporates the yield uncertainty risk, the random market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in the model.

The development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the uncertain risk of emission reduction investment. With the government's carbon emission constraints, green manufacturers implement the advance selling strategy to increase both profit and reduction level. However, few studies consider the consumer's green preference and emission constraints in advance selling market and spot market independently. The authors' paper investigates the optimal strategies of advance selling pricing and reduction effort for green manufacturers to maximize profits.

The authors' paper designs a stochastic model and investigates the manufacturer's optimal strategies of advance selling price and emission reduction efforts by categorizing different purchasing periods of low-carbon consumers. With the challenges of uncertain demand and government's emission constraints, the authors' develop the non-linear optimization model to investigate the manufacturer's profit-oriented decisions.

The results show the government's carbon constraints cannot influence the manufacturer's profit, but the consumer's low-carbon preference in the advance selling period can. Interestingly, the manufacturer will make fewer reduction efforts even when the consumers have stronger environmental awareness. In addition, the increasing consumer price sensitivity will exacerbate the profit loss from mandatory emissions reduction. Overall, for achieving a win–win situation between emission reduction and profit growth, green manufacturers should not only consider the sales strategies, market demand, and government constraints in a low-carbon market, but also pay attention to the uncertainty of green technology innovation.

With the consideration of the government's carbon emission constraints, uncertain demand, and low-carbon consumer's preferences, the authors' study innovatively incorporates the joint impacts of advance selling strategy and emission reduction effort strategy and then differentiates between two cases that pertain to the diverse carbon emission regulations.

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

Looks at the 2000 Employment Research Unit Annual Conference held at the University of Cardiff in Wales on 6/7 September 2000. Spotlights the 76 or so presentations within and shows that these are in many, differing, areas across management research from: retail finance; precarious jobs and decisions; methodological lessons from feminism; call centre experience and disability discrimination. These and all points east and west are covered and laid out in a simple, abstract style, including, where applicable, references, endnotes and bibliography in an easy‐to‐follow manner. Summarizes each paper and also gives conclusions where needed, in a comfortable modern format.

By designing a system dynamics model in the form of a multimodal transportation system, this study for the first time seeks to reduce costs and time, and increase customer satisfaction by considering uncertainties in the intra city transit system, especially demand uncertainty and provide a prototype system to prove the capability of the dynamical system.

The paper tried to model the factors affecting the intra city multimodal transportation system by defining different scenarios in the cause-and-effect model. The maps and results developed according to system dynamics modeling principles are discussed.

Four scenarios were considered given the factors affecting the urban transportation system to implement the transportation information system for reducing the material and non-material costs of wrong planning of the intra city transit system. After implementing the scenarios, scenario two was selected under the following conditions: advertising for cultural development, support of authorities by efforts such as street widening to reduce traffic, optimize infrastructure, increase and optimize public transport and etc.

The value of this paper is considering uncertainty in traffic optimization; taking into account behavioral and demand indicators such as cultural promotion, official support, early childhood learning, traffic hours and the impact of traveler social status; investigating the factors affecting the system under investigation and the reciprocal effects of these factors and real-world simulation by considering the factors and effects between them.