Pricing in Nonconvex Markets: How to Price Electricity in the Presence of Demand Response

2023 | Information Systems Research | Citations: 0

Authors: Bichler, Martin; Knörr, Johannes; Maldonado, Felipe

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices ... Expand

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices where no coalition of market participants wants to deviate. Walrasian prices do not exist in nonconvex markets in general, with electricity markets as an important real-world example. However, the availability of linear and anonymous prices is important for derivatives markets and as a signal for scarcity. Prior literature on electricity markets assumed price-inelastic demand and introduced numerous heuristics to compute linear and anonymous prices on electricity markets. At these prices, market participants often make a loss. As a result, market operators provide out-of-market side-payments (so-called make-whole payments) to cover these losses. Make-whole payments dilute public price signals and are a significant concern in electricity markets. Moreover, demand-side flexibility becomes increasingly important with growing levels of renewable energy sources. Demand response implies that different flexibility options come at different prices, and the proportion of price-sensitive demand that actively bids on power exchanges will further increase. We show that with price-inelastic demand there are simple pricing schemes that are individually rational (participants do not make a loss), clear the market, support an efficient solution, and do not require make-whole payments. With the advent of demand-side bids, budget balanced prices (no subsidies are necessary) cannot exist anymore, and we propose a pricing rule that minimizes make-whole payments. We describe design desiderata that different pricing schemes satisfy and report results of experiments that evaluate the level of subsidies required for linear and anonymous prices on electricity spot markets with price-sensitive demand.History: Xiaoquan (Michael) Zhang, Senior Editor; Pallab Sanyal, Associate Editor.Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grant BI 1057/1-9].Supplemental Material: The online supplement is available at https://doi.org/10.1287/isre.2022.1139. Collapse

Topics: price management anonymity accounting

Methods: experiment optimization model linear optimization descriptive statistic case study

Competitive DER Aggregation for Participation in Wholesale Markets

2023 | HICSS | Citations: 0

Authors: Chen, C.; Alahmed, A.; Mount, T.; Tong, L.

Abstract: The problem of the large-scale aggregation of behind-the-meter demand and gener ... Expand

Abstract: The problem of the large-scale aggregation of behind-the-meter demand and generation resources by a distributed-energy-resource aggregator (DERA) is considered. As a wholesale market participant, a DERA maximizes its profit while providing competitive services to its customers with higher consumer/prosumer surpluses than those offered by the distribution utilities or community choice aggregations.A constrained profit maximization program for aggregating behind-the-meter generation and consumption resources is formulated, from which payment functions for the behind-the-meter consumptions and generations are derived.Also obtained are DERA's bid and offer curves for its participation in the wholesale energy market. Collapse

Topics: price management competitive advantage

Methods: simulation case study

Understanding and managing the participation of batteries in reserve electricity markets

2023 | Decision Support Systems | Citations: 0

Authors: Naseri, Nastaran; Ghiassi-Farrokhfal, Yashar; Ketter, Wolfgang; Collins, John

Abstract: The proliferation of variable renewable energy increases the significance of res ... Expand

Abstract: The proliferation of variable renewable energy increases the significance of reserve markets, which provide energy flexibility to compensate for supply–demand mismatches on short notice. Accordingly, market regulators in different locations have been continuously adopting different mechanisms to attract flexible sources, such as batteries to participate in reserve markets. While these mechanisms are different and sometimes contradictory, there is no consensus on their effectiveness. Therefore, there is a dire need to examine their effectiveness systematically. This paper proposes a model-driven decision support system, focusing on regulators’ perspectives, assisting them in evaluating the effectiveness of different market mechanisms for attracting batteries in reserve electricity markets. We formulate the problem as a stochastic Mixed-IntegerLinear-Program, incorporating practical restrictions and applicable to many electricity markets. As an example, we apply our model to the case of the German electricity market to evaluate some market mechanisms. We find that although some reserve markets do not allow separate bidding in charging and discharging directions (asymmetric bidding), this seems to be an effective mechanism to attract battery operators. When asymmetric bidding is allowed, battery operators often optimally choose to reserve their capacity only in the charging direction and use the discharging direction in energy markets, taking opposite seller/buyer roles in energy and reserve markets. Our results also advocate mechanisms such as remunerating the activated energy and shifting the gate closure closer to the time of operation. In contrast, some existing rewarding mechanisms designed for flexible resources do not seem to be very effective. Collapse

Topics: decision support system accounting price management decision making non volatile memory

Methods: optimization model descriptive statistic mathematical model

Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

2022 | HICSS | Citations: 0

Authors: Kwon, Kyung-Bin; Zhu, Hao

Abstract: Effectively operating an electric vehicle charging station (EVCS) is crucial for ... Expand

Abstract: Effectively operating an electric vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. By utilizing the flexibility of EV charging needs, the EVCS can reduce the total electricity cost for meeting the EV demand. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces unfortunately grows with the number of EVs, which becomes very large and time-varying. This dimensionality issue affects the efficiency and convergence performance of generic RL algorithms. To this end, we advocate to develop aggregation schemes for state/action according to the emergency of EV charging, or its laxity. A least-laxity first (LLF) rule is used to consider only the total charging power of the EVCS, while ensuring the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Using the proposed aggregation scheme, the policy gradient method is applied to find the best parameters of a linear Gaussian policy. Numerical tests have demonstrated the performance improvement of the proposed representation approaches in increasing the total reward and policy efficiency over existing approximation-based method. Collapse

Methods: computational algorithm reinforcement learning literature study case study Markov decision process

Mechanism Design for Efficient Nash Equilibrium in Oligopolistic Markets

2022 | HICSS | Citations: 0

Authors: Lin, Kaiying; Wang, Beibei; You, Pengcheng

Abstract: This paper investigates the efficiency loss in social cost caused by strategic bi ... Expand

Abstract: This paper investigates the efficiency loss in social cost caused by strategic bidding behavior of individual participants in a supply-demand balancing market, and proposes a mechanism to fully recover equilibrium social optimum via subsidization and taxation. We characterize the competition among supply-side firms to meet given inelastic demand, with linear supply function bidding and the proposed efficiency recovery mechanism. We show that the Nash equilibrium of such a game exists under mild conditions, and more importantly, it achieves the underlying efficient supply dispatch and the market clearing price that reflects the truthful system marginal production cost. Further, the mechanism can be tuned to guarantee self-sufficiency, i.e., taxes collected counterbalance subsidies needed. Extensive numerical case studies are run to validate the equilibrium analysis, and we employ individual net profit and a modified version of Lerner index as two metrics to evaluate the impact of the mechanism on market outcomes by varying its tuning parameter and firm heterogeneity. Collapse

Methods: computational algorithm case study

Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

2021 | Business & Information Systems Engineering | Citations: 0

Authors: Fridgen, Gilbert; Körner, Marc-Fabian; Walters, Steffen; Weibelzahl, Martin

Abstract: To achieve a sustainable energy system, a further increase in electricity genera ... Expand

Abstract: To achieve a sustainable energy system, a further increase in electricity generation from renewable energy sources (RES) is imperative. However, the development and implementation of RES entail various challenges, e.g., dealing with grid stability issues due to RES’ intermittency. Correspondingly, increasingly volatile and even negative electricity prices question the economic viability of RES-plants. To address these challenges, this paper analyzes how the integration of an RES-plant and a computationally intensive, energy-consuming data center (DC) can promote investments in RES-plants. An optimization model is developed that calculates the net present value (NPV) of an integrated energy system (IES) comprising an RES-plant and a DC, where the DC may directly consume electricity from the RES-plant. To gain applicable knowledge, this paper evaluates the developed model by means of two use-cases with real-world data, namely AWS computing instances for training Machine Learning algorithms and Bitcoin mining as relevant DC applications. The results illustrate that for both cases the NPV of the IES compared to a stand-alone RES-plant increases, which may lead to a promotion of RES-plants. The evaluation also finds that the IES may be able to provide significant energy flexibility that can be used to stabilize the electricity grid. Finally, the IES may also help to reduce the carbon-footprint of new energy-intensive DC applications by directly consuming electricity from RES-plants. Collapse

Topics: Bitcoin green IS computer hardware energy information system edge computing

Methods: sensitivity analysis economic model computational algorithm optimization model machine learning

But Keep your Customers Closer: The Value of Regionality in Electronic Commerce

2021 | European Conference On Information Systems | Citations: 0

Authors: Menzel, Tobias; Teubner, Timm

Abstract: Green IS research addresses the pressing challenge of climate change. In partic ... Expand

Abstract: Green IS research addresses the pressing challenge of climate change. In particular, leading IS scholars have called for research on the design of IS solutions that support decision making for more sustainable practices. In this sense, our research explores how ICT artifacts should be designed to support consumer decision making in favor of regional products and services. We employ hedonic price regressions to analyze consumer preferences for regional products and services based on actual market data from an online price comparison platform. We find that consumers are willing to pay mark-ups for regional providers as long as those are both 1) based in close proximity to consumers (geographic regionality) and 2) strongly connected with and within the region (entrepreneurial regionality). We contribute to the Green IS debate by deriving tangible implications for the design of different types of user interfaces such as websites and comparison platforms. Collapse

Topics: user interface decision making price management website systems design

Methods: regression analysis method data modeling ordinary least square design artifact F-test

RTO Governance Structures Can Affect Capacity Market Outcomes

2020 | HICSS | Citations: 0

Authors: Blumsack, Seth; Yoo, Kyungjin

Abstract: Regional Transmission Organizations (RTOs), which coordinate delivery for over t ... Expand

Abstract: Regional Transmission Organizations (RTOs), which coordinate delivery for over two-thirds of the electricity consumed in the U.S., are required by the FERC to employ stakeholder-driven mechanisms to establish market and operational rules. These “governance structures” set up a quasi-political process for determining which market rules are adopted and which are not. This study shows how governance systems are not simply administrative constructs but have real impacts – the details of how the market rules are made will ultimately affect market outcomes. Using the capacity market in the PJM Interconnection as a case study, we model the preferences of individual stakeholders over different capacity market designs, under different decision rules for which capacity market design is implemented. We compare capacity market design choices under PJM’s current decision system, which requires a super-majority in a sector-weighted voting context to implement a new market rule, with the decision systems used in the New York ISO and also under systems of preferential voting. This voting model is integrated with a model of capacity market clearing which allows us to demonstrate how different decision systems matter in terms of installed capacity and capacity market outcomes. Collapse

Topics: IS impact

Methods: sensitivity analysis simulation case study Monte Carlo simulation

Theories: game theory

Secondary Use of a Smart Electric Container Terminal: Scenario Analysis on relevant Cost Drivers

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Harnischmacher, Christine; Greve, Maike; Willnat, Mathias; Kolbe, Lutz

Abstract: Increasing the sustainability of the transportation sector yields great potenti ... Expand

Abstract: Increasing the sustainability of the transportation sector yields great potential in the fight against climate change. Container terminal logistics is one of the main potential areas to focus on, as the heavy-duty vehicles involved in container transportation can be operated electrically. Incorporating information systems into this setting, this is a promising area for smart grid applications. Free battery capacities of vehicles, that are currently unused, can serve as frequency stabilizing flexibilities in the energy grid. Against this background, this paper provides insight into the configuration of costs drivers that arise in the context of the implementation of a virtual power plant in a container terminal by means of a structured scenario analysis approach. Collapse

Topics: brainstorming logistics management green IS energy information system information technology infrastructure

Methods: group interview qualitative interview literature study structured literature research literature sample

Electric Vehicle Charging Decisions using Only Market Trends with Persistence

2020 | HICSS | Citations: 0

Authors: Hammerstrom, Donald J; Pratt, Richard M

Abstract: Electric vehicle (EV) charging can take advantage of real-time electricity mark ... Expand

Abstract: Electric vehicle (EV) charging can take advantage of real-time electricity market price volatility. Presuming that an EV must be fully charged at a future target time, the EV should choose to charge using the lowest future electricity prices and thereby minimize electricity cost. Statistical methods must be used if forward prices are unavailable. In this case, historical prices and trends must be mined to anticipate which prices should be used to charge the EV. Price persistence, a tendency for electricity prices to inexplicably become and remain relatively high or low for extended durations, is particularly difficult to forecast and mitigate. This paper formulates and tests a pragmatic strategy for integrating conventional static statistical prices and the Bayesian propagation of price persistence from the current price to prices in the current and future hours. Simulations were conducted to test the cost effectiveness of charging strategy using real-time electricity prices. 1 Collapse

Topics: price management programming language website

Methods: simulation longitudinal research autoregressive integrated moving average autocorrelation analysis autoregressive conditional heteroscedasticity model