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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.

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.

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.

To promote the development of renewable energy, the Chinese Government adopts the policy of Feed-in Tariff and subsidy. However, the high purchase price and the intermittence limit the development of renewable energy source electricity (RES-E). The purpose of this paper is to discuss the pricing strategy for system operators to stimulate the development of the RES-E industry under the scenario of uncertain supply and demand.

The authors establish a two-echelon supply chain investment model led by a power grid operator considering the uncertainties in both demand and supply, and study the impact of the power purchase price designed by a system operator using Stackelberg’s model.

There is an optimal capacity for RES-E generators, that is, independent of the market demand. Besides, the optimal order of grid operators is independent of the uncertain RES-E supply and the purchase price of fossil fuel. By properly setting the purchase prices, the system operator can stimulate the capacity investment in renewable energy. Finally, increasing the punishment in power shortage can stimulate the capacity investment in RES-E under certain conditions.

The result of this paper can mitigate the phenomenon of power abandonment in the RES-E industry and promote the grid integration of RES-E.

Both uncertain demand and supply are considered in this paper. A heuristic algorithm is provided to compute the optimal purchase price combination.

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 electric power industry has been moving from a regulated monopoly structure to a deregulated market structure in many countries. The purpose of this study is to comprehensively review the existing markets to study advantages, issues involved and lessons learnt to benefit emerging electricity markets.

The paper employs a comprehensive review of existing competitive electricity market models in USA (California), UK, Australia, Nordic Countries (Norway), and developing country (Chile) to analyze the similarities, differences, weaknesses, and strengths among these markets based on publically available data, literature review and information.

Ongoing or forthcoming electricity sector restructuring activities in some countries can be better designed based on lessons learnt from existing markets and incorporating their own political, technical and economical contexts. A template for design of successful electricity market has also been presented.

This study is limited to a comparative analysis of five markets and can be extended in the future for other existing and emerging electricity markets.

The discussed weaknesses and strengths of existing electricity markets in this study can be practically utilized to improve the electricity industry market structures leading to several social benefits including lower electricity cost.

The comprehensive review and analysis of five existing markets, physically located in different continents, may be used as an assistance or reference guide to benefit the emerging electricity markets in other countries.

The purpose of this study is to develop a set of parameters universally acceptable for assessing design and construction strategies for reducing operational energy usage and its associated greenhouse gas (GHG) emission. Also, the parameters are intended to estimate the quantity of energy and its associated GHG emission reduction over the assessment period.

This study used five steps framework comprising definition of purpose, selecting the candidate parameters, criteria selection and description, selecting proposed parameters and defining the proposed parameters. The criteria used were the parameter’s prevalence, measurability, preference and feasibility toward adaptability to the relevant stakeholders.

This study consolidated 11 parameters. Seven cover designs and construction strategies comprising energy monitoring, natural lighting and ventilation design. Others are building thermal performance, efficient equipments, renewable energy and energy policy. The remaining four consider operational energy consumption, GHG emission quantification and their reduction over time.

Providing suitable indicators for assessing direct and indirect GHG emission with easily accessible data is essential for assessing built environment. The consolidated parameters can be used in developing rating systems, monitoring GHG inventories and activities of building related industries.

This study was conducted at the CEIES UTHM and used 11 existing rating systems open for research purposes, International Panel for Climate Change reports and GHG protocol report and guides and several other standards.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

The purpose of this paper is to examine the point of convergence between the process of drawing up the intellectual capital statement (ICS) and the corporate social responsibility statement (CSRS).

The theoretical paradigm underlying the authors’ research is interpretivism. In particular, this study is conducted in light of the “action research” perspective. Data have been gathered by in‐depth interviews with managers and from group discussions.

The results demonstrate that is possible and also convenient to overlap the tasks to be done in the process of drawing up the ICS and the CSRS.

This paper is of interest to both scholars and professionals because it presents the results of an empirical study into the critical success factors for implementing the development of ICS and simultaneously of CSRS.

Environmental capabilities, allowing companies to carry out their productive activities in ways that limit damage to natural environment, are at the heart of the fourth stage of research in intellectual capital. Accordingly, the purpose of this research is to explore firm's current environmental capabilities, disclosed by managers through corporate messages, that participate to the development of sustainable intellectual capital (SIC).

With this in mind, we first conducted a lexical content analysis followed by a thematic content analysis of 241 letters to shareholders from the CEOs of major European companies published in 2016.

The lexical content analysis reveals that managers of major European companies have developed green alliances to address the energy transition challenge by modifying their manufacturing processes. The thematic content analysis of the CEOs discourse highlights that managerial competencies, continuous innovation and stakeholder integration are key environmental capabilities that matter to CEOs.

This study contributes to the fourth stage of research on IC highlighting the environmental capabilities and resources that are disclosed by companies in their corporate communication. Our results enhance the understanding on how environmental capabilities and resources enhance the human, organizational, technological and relational sustainable intellectual capital.

This research highlights the importance of green alliances that allow companies to address the challenge of the ecological transition. In this context, the continuous innovation capability seems to be a fruitful way of gaining competitive advantage in this challenge.

This paper provides a detailed description of the environmental capabilities that participate to the development of the human, technological and relational SIC.