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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.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Past research demonstrated that novel IT-based business models generate tremendous returns for innovators. However, the risks associated with these innovations remain under-explored. This paper aims to address this critical gap analyzing risks and offering important insights particularly for practitioners.

The authors adopted an exploratory multiple-case study research design. It draws on 22 semi-structured interviews with managers from leading energy utilities, as well as leading providers of virtual power plants technology within the German energy industry.

The research reveals that main risks in new digital business models in the energy sector are associated with three forms of interdependence between innovation actors: the regulatory, the technological and the collaborative. To deal with these interdependencies, the authors propose an original multi-step risk management framework. This framework considers the outreach as a critical dimension for risk assessment and offers a new risk response matrix to draw individual and collective mitigation activities for specific types of risks.

This paper offers a framework for the management of interdependence risks that are fundamental for business model innovations based on IT. Thus, it is applicable in companies both inside the energy sector and beyond.

This paper analyzes an important digital business model innovation that has not yet been explored in management literature – the virtual power plant (VPP). It is based on original and current empirical work and proposes a novel risk management framework for business organizations.

This paper aims to: first, it studies expert opinions about the future of clean, decentralized energy technology in Australia; second, develop an interpretive and participatory foresighting methodology for a forthcoming study.

This paper reports a forecasting study about the future of clean energy. Driven mostly by economics and changing carbon policies, the energy sector is currently moving from fossil fuels to a variety of cleaner technologies. Energy experts have several incommensurate interpretations of how this change will happen. This paper describes the first phase of an ongoing study that foresight clean energy futures in Australia. By building on a participatory method in a scientific expert community, it describes the path from technological presumptions into four parallel yet interconnected scenarios. The paper also explores the social drivers behind these scenarios.

First, energy experts in Australia classify futures into four main scenarios: abundant, where energy will be mostly produced by solar cells; traded, where the future of energy lies in virtual power plants and microgrids; circular, which targets Australia’s NetZero goals through biomaterials, carbon capture and new powerful; secure, which secures the country’s energy supply through coal and nuclear energy. Second, they locate policy as the most important form of wildcards. The policy is multilayered from local to US politics and falls outside the scope of forecasting.

The most important limitations of the study are: first, its reliance on scientific and technological experts, which guarantees its scientific validity but may underrepresent the social drivers of energy; second, this study is a methodological pilot of a larger study that will target industrial, commercial and local drivers; third, its focus on Australia, where politics, the size of the country and climate shape the uptake of clean energy in specific ways, most notably in the case of rapid uptake of solar energy.

The main practical implications of the paper are its broad focus on clean energy futures and its participatory foresighting approach, which can be repeated in other studies.

The main social implication of the study is that it clearly shows that a technological perspective is necessary but not sufficient in understanding the future of clean energy. The paper also shows that local drivers importantly mold the future and should be taken into account in future studies and policy.

This paper makes two contributions. First, it organizes several technologies into four scenarios that clarify Australia’s clean energy futures better than a piecemeal study would do. Second, it developed and piloted an interpretive participatory methodology for studying futures by building on references from design research. This methodology will be used in subsequent studies.

Pumped-storage hydroelectricity (PSH) is considered as an effective method to moderate the difference in demand and supply of electricity. This study aims to understanding of the high capacity of energy production, storage and permanent exploitation has been the prominent feature of pumped-storage hydroelectricity.

In this paper, the optimization of energy production and maintenance costs in one of the large Iranian PSH has been discussed. Hence, a mathematical model mixed integer nonlinear programming developed in this area. Minimizing the difference in supply and demand in the energy production network to multiple energies has been exploited to optimal attainment scheme. To evaluate the model, exact solution CPLEX and to solve the proposed programming model, the efficient metaheuristics are utilized by the tuned parameters achieved from the Taguchi approach. Further analysis of the parameters of the problem is conducted to verify the model behavior in various test problems.

The results of this paper have shown that the meta-heuristic algorithm has been done in a suitable time, despite the approximation of the optimal answer, and the consequences of research indicate that the model proposed in the studied power plant is applicable.

In pumped-storage hydroelectricity plants, one of the main challenges in energy production issues is the development of production, maintenance and repair scheduling concepts that improves plant efficiency. To evaluate the mathematical model presented, exact solution CPLEX and to solve the proposed bi-objective mixed-integer linear programming model, set of efficient metaheuristics are used. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7%–12% and effective optimization processes.

This paper aims to identify the effects of client orientation on the business models of central power generation companies.

Five major Russian wholesale electricity market players have been selected for the analysis conducted by applying the “business model canvas”. To identify the changes induced by client orientation, the progress of companies’ business models has been traced over six years, from 2009 to 2015.

Five major trends in business model changes because of client orientation have been identified: declaration of the movement toward client orientation and adoption of client service standards; emergence of business diversification in favour of engineering, construction, service, operation and maintenance of power-generating facilities; increase in vertical integration; increase in the diversity of communication channels with consumers; and increase in the diversity of customer relationships. The results have been compared with those obtained from international studies. The conclusions about international and local characters of the trends are presented.

The study contributes to the knowledge of the current and upcoming changes in the business of central power generation companies triggered by the advent of electricity prosumers. The results are valuable for both management decision makers and theorists.

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa.

The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior.

This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions.

It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households.

Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers.

This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

This paper analyses the justification of technological choices and options in the context of nuclear energy policy. We argue that “society” increasingly demands a justification with regard to the level of uncertainty and inequality a certain technological choice induces. We aim to demonstrate that policy makers in fact do address these issues, but depending on how they define the problem, this is done in a more explicit (overt) or implicit (covert) way.

First, the changing context with regard to the justification of technological choices is briefly sketched. We draw the attention to the link between the way a certain (energy) policy problem is defined, and the way the framework for political decision‐making is set up in response to the problem. In order to clarify this observation, we make use of a scheme derived from policy sciences, mapping out policy problems in two dimensions: the (lack of) certainty concerning the kinds of knowledge a problem may require, and the (lack of) consensus on relevant values (i.e. “the common good”, “basic rights”, etc.). Each type of policy problem requires a distinct solution strategy. A so‐called Type III‐error occurs when the wrong problem is solved by employing a strategy which does not apply to the problem at hand. In that case, political theory predicts strategic behavior in order to actively suppress or blur value differences. The Belgian decision to phase out nuclear power is used as a case study to illustrate some of the theoretical implications of the scheme.

Several Type III‐errors could be demonstrated in the case of the Belgian phase out. In this case, social learning was severely hampered by different methodological approaches; lack of data; different perceptions of relevant time scales; different framing of the problem; institutional barriers; lack of communication; strategic use of scientific assessments by different stakeholders; and insufficient knowledge of scientific assessments.

The paper aims to broaden the debate on energy policy outside the boundaries of institutional decision‐making. We conclude with some practical recommendations for future energy policy, regarding problem structuring, defining possible options, goal and strategy formulation and monitoring of choices.

Power outages which may be triggered, for example, by natural hazards and system failures are a common phenomenon, associated with large impacts on society including the healthcare sector. Minimising adverse impacts effectively requires an analysis of possible impacts and the identification of measures aiming at reducing vulnerability and increasing resilience.

To systematically identify impacts as well as preparation, mitigation and recovery (PMR) measures, a moderated workshop with participants representing different healthcare sub‐sectors in Germany was conducted and complemented by semi‐structured interviews and a thorough analysis of literature. Impacts were determined for three scenarios of power outage duration, <8, 8‐24 and >24 hours.

Whereas hospitals are in general well prepared with respect to shorter outages, due to obligatory emergency power in Germany, outpatient medical care, nursing homes (NH) and, in particular, home‐care nursing are early affected. Failure of these sub‐sectors puts additional strains on hospitals. If outages last more than one day and are associated with failure of other critical infrastructures (CIs), especially water supply, hospitals may be severely affected. Effective preparation and mitigation measures identified based on a facility‐specific impact analysis, as well as good cooperation between actors, may reduce impacts.

The largely case‐study‐based literature is complemented by a systematic and extensive analysis of direct and indirect impacts on the main healthcare sub‐sectors in Germany, followed by an identification of specific PMR measures. As a novelty outage duration is explicitly accounted for. Also, interdependencies between the healthcare sub‐sectors as well as dependencies on other CI are considered.