Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind ...turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Power TAC (www.powertac.org) is a discrete-time competitive simulation that models a retail electricity market. Since 2012 it has been the foundation of an annual competition, challenging teams from ...around the world to build autonomous trading agents that communicate with the simulation over the internet. These “retail brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Hourly differences between wholesale market positions and net consumption by their subscribed customers must be cleared in a local balancing market using a combination of demand response and wholesale balancing energy. The simulation server is open source and highly modular, designed to be accessible to inexperienced student developers. It makes heavy use of annotations and aspect-oriented programming to achieve consistency and ensure that all important events are recorded, allowing simulations to be re-played and analyzed in depth.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The recent advent of electric vehicles (EVs) marks the beginning of a new positive era in the transportation sector. Although the environmental benefits of EVs are well-known today, planning and ...managing EV charging infrastructure are activities that are still not well-understood. In this paper, we are investigating how the so-called EV-enabled parking lot, a parking lot that is equipped with a certain number of chargers, can define an appropriate parking policy in such a way that satisfies two challenges: EV owners' needs for recharging as well as the parking lot operator's goal of profit maximization. Concretely, we present three parking policies that are able to simultaneously deal with both EVs and internal combustion engine vehicles. Detailed sensitivity analysis, based on real-world data and simulations, evaluates the proposed parking policies in a case study concerning parking lots in Melbourne, Australia. This paper produces results that are highly prescriptive in nature because they inform a decision maker under which circumstances a certain parking policy operates optimally. Most notably, we find that the dynamic parking policy, which takes the advantage of advanced information technology (IT) and charging infrastructure by dynamically changing the role of parking spots with chargers, often outperforms the other two parking policies, because it maximizes the profit and minimizes the chance of cars being rejected by the parking lot. We also discuss how making a few parking spots EV-exclusive might be a good policy when the number of available chargers is small and/or the required IT infrastructure is not in place for using the dynamic policy. We conclude this paper proposing a technology roadmap for transforming parking lots into smart EV-enabled parking lots based on the three studied parking policies.
With an increasing share of renewable energy resources participating in electricity markets, there is a growing dependence between renewable power production and clearing prices of spot markets. ...Modeling this dependence using bivariate analysis can result in underestimation of market risks and adverse effects for stakeholders’ risk management. To enable an undistorted risk assessment, we are using a copula approach to precisely and jointly model electricity prices and infeed volumes of wind power. We simulate the case of wind farm operators using power purchase agreements (PPAs) to shift the price risk to an energy trader, who integrates the infeed into its portfolio. The trader’s portfolio can either be geographically dispersed, or highly localized. Based on its portfolio, the energy trader can decide to use derivatives such as futures to manage its risk exposure. The trader decides on future volumes subject to its portfolio’s inherent volatility. With a given risk averse strategy, a sufficiently diverse portfolio can help reduce the necessity to trade futures and subsequently the disadvantage of having to pay potential risk premiums.
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Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs ...enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.
Renewable energy cooperatives (RECs) are an important element of the European energy transition. Allowing citizens and companies to invest in renewable energy and thereby become independent power ...producers has advanced the acceptance of renewable energy among the population, accelerating the move toward a more decentralized and sustainable power supply. We investigate how a REC could be designed to increase renewable energy deployment at the Port of Rotterdam. Based on a progressive case study conducted among a wide array of stakeholders at the Port and across Europe, we have found 14 specific characteristics a REC should embody to facilitate the energy transition at the Port. Based on these results, we present an action plan with concrete recommendations on how a successful cooperative could be launched at the Port. The results of this research can serve as a guide for stakeholders in any industrial cluster interested in driving the energy transition through a REC.
•Renewable energy cooperatives (RECs) are important elements accelerating the energy transition.•A progressive case study develops 14 specific characteristics a REC should embody.•Action plan: how to launch, with whom to launch, how to manage, and how to sustain operations of a REC.
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We show how an electricity customer decision support system (DSS) can be used to design effective demand response programs. Designing an effective demand response (DR) program requires a deep ...understanding of energy consumer behavior and a precise estimation of the expected outcome. Excessive demand shifting or a high price responsiveness might create new peaks during low-demand periods. We combine insights from a real-world pilot with simulations and investigate how we can design effective DR schemes. We evaluate our pricing recommendations against existing economic approaches in the literature and show that targeted recommendations are more beneficial for customers and for the grid. Furthermore, we conduct robustness tests in which we apply our methods on two independent datasets and observe differences in peak demand and electricity cost reduction, dependent on individual characteristics. In addition, we examine the role of energy policy, as it varies across countries, and we find that the presence of competition in the electricity market creates lower prices and more cost savings for individuals. Finally, we measure the economic value of our DSS and show that our DSS can result in up to 38% savings on household electricity bills. Our results exhibit how the design of effective DR can be achieved and provide insights to energy policymakers with regard to understanding consumers' behavior and setting regulatory constraints.
•A data-driven approach to designing successful DR schemes is presented.•Price sensitivity and awareness influence DR effectiveness.•High price sensitivity yields electricity cost savings but not always peak reduction.•Based on behavioral characteristics, personalized DR recommendations are outlined.•Segmenting consumers and providing different prices is effective for the grid.
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Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for ...global populations, and poses significant economic costs. However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence (AI)-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric (CASE) vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. This article contributes a framework to direct research and practice toward leveraging the opportunities afforded by CASE for a more efficient and less environmentally problematic mobility system. The authors propose seven overarching dimensions of action. These range from designing real-time digital coordination mechanisms for the management of mobility systems to developing AI-powered real-time decision support for mobility resource planning and operations. Per each dimension, concrete angles of attack are suggested which, we hope, will spur structured engagement from both researchers and practitioners in the field.
Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs (e.g., due to traffic congestion). However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence–powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. The resulting layered modular architecture is similar to that seen in other cyber-physical systems. Yet, it also comes with several characteristics and challenges that are unique to the domain of mobility and require entirely new solution approaches. Although other management and domain-specific research disciplines have started to embrace the new opportunities for research resulting from this deep structural change, the information systems (IS) community’s involvement in smart mobility research has been marginal. Yet, we argue that our field’s uniquely multidisciplinary, data-driven, and socio-technical research lens puts it in a strong position to address many of the large-scale societal challenges encountered in the mobility sector. Therefore, we make the case for IS research to play an active role in delivering a smart sustainable mobility ecosystem that is beneficial to users, mobility providers and the environment. We contribute a research framework to direct IS research efforts while providing a shared understanding of the smart sustainable mobility domain. We also present seven IS research opportunities along the dimensions of this framework and propose concrete angles of attack which we hope will spur an impactful and structured research agenda in the area.
History:
Ahmed Abbasi, Senior Editor; Gautam Pant, Associate Editor.
Supplemental Material:
The online appendix is available at
https://doi.org/10.1287/isre.2022.1167
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In the battle against climate change, electrification plays an increasingly large role in our society. The growing use of electricity networks requires advanced coordination mechanisms to avoid the ...tragedy of the commons. In this paper, we explore the effects of optimum time-dependent pricing (TDP) on supplier surplus within electricity markets, using a parameterized optimization model. Varying not only the prices themselves, but also their announcement horizon, we show how the suppliers’ optimal decision depends on risk aversion, forecasting quality, and end-user flexibility. The inclusion of procurement risk in our model shows that TDP can be beneficial for suppliers when they want to actively manage risk, even if expected profits are lower. At the same time, the shifting of end-user demand to low-cost times reduces the overall system cost, and potentially carbon emissions.
To comply with sustainability goals, many companies buy green energy to serve their energy demand. This is typically done by engaging in bilateral power purchase agreements (PPA) with renewable ...energy producers (REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially attractive for sellers, providing price certainty, unlike trading in electricity markets. However, PPAs can bring quantity uncertainty for buyers due to the uncertainty of future green energy delivery. This uncertainty in the long-term endangers sustainability targets, and in the short-term complicates reliable and cost-efficient demand matching. Thus, multiple strategies have been used in PPAs to encourage sellers to provide accurate and good-faith predictions of their short-term and longer-term future production. Yet, it has been shown that REPs can have incentives to misreport predicted values. This has discouraged some companies from engaging in PPAs. In this paper, we first investigate how PPA structure and pricing can incentivize REPs to provide more reliable predictions. This shifts the risk of production uncertainty to REPs, increasing the chance that REPs adopt batteries. We further study how having batteries for REPs affects their own revenue as well as the reliability of their energy predictions for buyers. We use analytical and simulation approaches to propose a decision tree for a win-win PPA structure, which improves reliability for buyers while maintaining profitability for REPs.
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