Figure 2 - uploaded by Florian Kühnlenz
Content may be subject to copyright.
Load curve examples of an optimizing, flexible user and an ordinary user in the RTP regime. The optimizing user shifts his load by adjusting the phase of the sine curve to have minimum correlation with the price curve. Note: the usage is scaled and not zero based.
Source publication
This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs, which in turn contradicts the...
Context in source publication
Context 1
... usage. This flexibility can be utilized to shift power consumption away from high-price times to low-price ones to minimize consumer costs. As a simplification, the usage patterns are modeled as sine curves so that optimizing agents need to set the phase of their respective sine curve to simulate a shift in the usage pattern, illustrated in Fig. 2. This does not change the shape of the use, but rather simulates the shift of peak power to a different time of the day. This is also consistent with the rebound effect [37], which considers that a reduction of the load during one part of the day is usually followed or preceded by a higher consumption to compensate for the reduced ...
Citations
... During the recent few decades, a continuous evolution in power system infrastructure leading to abrupt nonlinearity due to the involvement of electronic load on the demand side. Because of its nonlinearity nature, a load is continuously changing after every brief interval of time [1]. Mainly, in summer the energy demand is high during peak hours than off-peak hours because of maximum commercial and air conditioning activities. ...
Under the umbrella of a smart grid environment, demand response (DR) is a comprehensive way to make the best use of household energy consumption. DR refers to the rescheduling of household energy consumption in response to the pricing signal received from the utility. Interoperability enables the DR loads to communicate with the utility through a smart meter to plan the energy consumption according to the pricing signal received. This paper presents an exclusive tri-objective model that considers real-time data of different parameters like peak, off-peak demand, and time of use pricing signal and performs optimal scheduling to reduce the overall energy expenses, peak-to-average power ratio (PAPR), and improve the load factor (LF) of the system over the entire horizon. Proposed planning considers the least, and highest priority loads for an optimal implementation of proposed planning. The proposed planning is implemented for a time horizon of 30 days. A price-responsive DR model is effectively implemented based on time of use tariff regulation. Based on predefined parameters, a proposed algorithm shifts the least priority load from peak hours to off-peak hours without disrupting the highest priority. Proposed planning is a Heuristic problem tackled as Mixed integer linear programming, mathematically solved in MATLAB. The results demonstrate that; the proposed planning effectively reduces the cumulative energy expenses by 13.36% for the end customer, improves the LF of the system by 5.11%, and reduces PAPR by 15.22% to improve the stability margin of the system. Finally, sensitivity analysis has been performed based on varying peak tariffs which shows the effectiveness of the proposed approach. The results clarify the effectiveness of the proposed model for both customer and utility ends.
... A stochastic multi-objective unit commitment real-time DR model with resilient optimization is presented in [12]. In [13], the possible use of price-response dynamics for limiting power usage is demonstrated with a one-way price signal. In a bilevel optimization model offered in [14], the grid efficiency is improved via setting time-differentiated power rates by a provider and reducing electricity consumption. ...
... Zhang et al. (2019), which presents a novel two-stage trading mechanism for direct market participation of large-scale consumers. A typical application of such models is to aggregate market participants' bids and model their impact on market clearing, to provide a deeper understanding of the impact of market participation on market outcomes (Kühnlenz et al. 2018). This is referred to here as a market-centric approach, as results are particularly useful for market regulators implementing market regulation changes to improve market participation. ...
The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context.
... However, the real challenge is to include EDF as a market participant in the models as the bunch of services that EDF can provide are much wider than the implicit one. Hence, models presented in Table 3 focus on EDF integration, though [82] and [83] include both. ...
... Long-term consideration usually corresponds with 'Generation expansion planning models' and can be daily or hourly scheduled (with a lot of simplifications, such as representative weeks or months for each season). For short-term planning there are three main markets: day-ahead (DA) [90], intra-day [82] and real-time (RT) [91]. For DA, hourly schedule is used, and for RT, sub-hourly timing is considered. ...
... ✓ Flexibility remuneration mechanism: In addition, to encourage EDF providers to participate in the markets, cost avoidance analysis presented in [82] [90] [101] are not enough, remuneration mechanisms for flexibility products are necessary. However, they are still not well developed nor clear the best way to do so. ...
At operational level, fossil fuel phase-out and high shares of non-dispatchable renewable energy resources (RES) will challenge the system operator’s (SO) ability to balance generation, and the demand at any time. The variability of RES output ranges from one hour to a season, and critical events such as low supply and high demand might occur more frequently and for more extended periods. When evaluating the role of Energy Storage Systems (ESSs) in this context, the need for a long time scope to capture the different RES variabilities must be reconciled with the need for modeling the hourly chronology. This paper presents a medium-term operation planning model, addressing both the energy dispatch and the balancing services. This study shows that representing the combined chronological variability of demand and RES production is essential to properly assess the roles of different kinds of ESSs in the future 2030 electricity mix. Otherwise, it would not be possible to appropriately capture the frequency, depth, and length of events for which ESSs are activated. The analysis also highlights the importance of considering balancing services, given the significant contribution of batteries to the reserve market. Finally, the results show that batteries and Pumped Storage Hydro (PSH) have different roles in the Spanish electricity system with a high renewable penetration. While PSH is mainly used to provide energy during critical periods, batteries mostly provide balancing services.
... However, the real challenge is to include EDF as a market participant in the models as the bunch of services that EDF can provide are much wider than the implicit one. Hence, models presented in Table 3 focus on EDF integration, though [82] and [83] include both. ...
... Long-term consideration usually corresponds with 'Generation expansion planning models' and can be daily or hourly scheduled (with a lot of simplifications, such as representative weeks or months for each season). For short-term planning there are three main markets: day-ahead (DA) [90], intra-day [82] and real-time (RT) [91]. For DA, hourly schedule is used, and for RT, sub-hourly timing is considered. ...
... ✓ Flexibility remuneration mechanism: In addition, to encourage EDF providers to participate in the markets, cost avoidance analysis presented in [82] [90] [101] are not enough, remuneration mechanisms for flexibility products are necessary. However, they are still not well developed nor clear the best way to do so. ...
Current power systems are characterized by the increase of renewable generation and distributed energy resources introducing more variability on the generation and enhancing the importance of the management in the consumption side. In this paper, a thorough review about the explicit demand flexibility (EDF) concept is addressed. This review, firstly, brings clarification over the different terms that have been used in the literature and the agents that are involved in the demand flexibility framework. Secondly, analyzes the different balancing services where EDF could participate, identifying the main barriers found for each market. In addition, it contributes to classify how mathematical models include EDF participation in ancillary services and congestion management, finding the main weaknesses and working lines for EDF integration in such models. Finally, a European overview is assessed to see where flexible resources have actual participation and how it is performed.
... Although rich in technology detail, exogenously specified end-use demands restrict the feedback effect on the consumer side. Exposing electricity end-users to varying prices inevitably results in behaviors that maximize consumer welfare [10]. ...
Energy system optimization models (ESOMs) are designed to examine the potential effects of a proposed policy, but often represent energy-efficient technologies and policies in an overly simplified way. Most ESOMs include different end-use technologies with varying efficiencies and select technologies for deployment based solely on least-cost optimization, which drastically oversimplifies consumer decision-making. In this paper, we change the structure of an existing ESOM to model energy efficiency in way that is consistent with microeconomic theory. The resulting model considers the effectiveness of energy-efficient technologies in meeting energy service demands, and their potential to substitute electricity usage by conventional technologies. To test the revised model, we develop a simple hypothetical case and use it to analyze the welfare gain from an energy efficiency subsidy versus a carbon tax policy. In the simple test case, the maximum recovered welfare from an efficiency subsidy is less than 50% of the first-best carbon tax policy.
... This assumption gives results which are valuable for early adopters when evaluating the economic viability of market entry, as historic data ensures realistic scenarios. However, as the share of flexible industrial consumers participating in the market increases, updated or forecasted market prices would have to be used instead, as increasing flexible demand could create undesirable load peaks from consumer synchronisation at times of low prices (Kühnlenz et al. 2018;Krause et al. 2015). ...
In a deregulated market context, industrial consumers often have multiple market participation options available to bid their flexible consumption in electricity markets and thereby reduce their electricity bill. Yet most participation strategies for demand response are developed in a fixed and predefined set of submarkets. Meanwhile, little literature has compared multiple market options for market participants. Therefore, this paper proposes a comparative approach between available market options to evaluate savings from different market participation options. More specifically, this study implements an optimisation program in Python to investigate the impacts of changes in an industrial process’ flexibility on savings with different market participation options. The optimisation program is tested with a case study of an industrial cooling process in three Danish submarkets (day-ahead, intraday, and regulating power markets). The market participation options are formed by different combinations of these three submarkets, and the type and amount of process flexibility are varied by changing time and load constraints in the optimisation program. The results show that bidding in market options with multiple submarkets yields higher savings than single-market bidding, but that increases in available flexibility impact savings in each market option differently. Increased flexibility will only bring additional savings if it allows to take further advantage of price variations in a market option. Additionally, increases in savings with flexibility depend on the considered type of flexibility. These changes in relative savings between market options at each flexibility level imply that the optimal market option is not a static choice for a process with variable operating conditions. The optimal market option for an industrial consumer depends not only on market price signals, but also on the type and amount of available flexibility.
... The objective functions in most DR models encompass economical and technical conditions: quality of service, peak power demand, utility associated with electricity consumption, and discomfort associated with existing consumption patterns (Afşar et al., 2016;Soares et al., 2019;Aussel et al., 2020;Soares et al., 2020;Luo et al., , 1996Ruiz et al., 2018). For instance, an agent-based model that incorporates both balancing and spot power markets is presented in Kühnlenz et al. (2018). A stochastic multi-objective unit commitment (2014) and Cicek and Delic (2015) incorporate welfare from electricity consumption using customers' utility function and generation cost with constrained optimization. ...
Designing the electricity market with efficient models and methods is essential for market participants and for the operation of the electricity system reliably. In this dissertation, we develop electricity market models and methods with high renewable penetration, emission trading, demand response, and address risk aversion with financial contracts under uncertainty.
The goal of the thesis work is to develop models and methods to help electricity generators, system planners, retailers, and end-consumers to identify optimal decisions using mathematical models and market instruments.
In the first chapter, we propose a game-theoretical equilibrium model that characterizes the interactions between oligopolistic generators in a two-stage electricity market with high penetration of renewable resources. The spot market equilibrium outcomes are derived analytically, and this allows the posterior numerical solution of the overall equilibrium problem. We introduce different types of contracts in the futures market to evaluate their performance and impact on the equilibrium market outcomes and how the outcomes depend on the levels of renewable penetration in the system.
In the second chapter, the European Union Emission Trading Scheme (EU ETS) is introduced with a market-based auctioning with financial contracts in the equilibrium electricity market model aiming at greenhouse gas (GHG) emission reduction. The auction-based allocation of emissions with allowance trading is examined and explored whether it brings economic efficiency by negating windfall profits that have been resulted from grandfathered allocation of allowances. Moreover, a coherent risk measurement is applied to model both risk averse and risk neutral generators and a stochastic optimization setting is introduced to deal with the uncertainty.
Finally, we apply a demand response program in the electricity market where market entities can manage uncertainty considering the demand-side. We develop a retailer-consumer electricity market model so that both players optimize their respective objective functions with respect to certain constraints both with market power and with perfect competition. The retailer’s problem with market power is solved using the consumer’s Karush-Kuhn-Tucker (KKT) optimally conditions entered as constraints in the retailer’s problem so that a mathematical program with equilibrium constraints (MPEC) problem that can be solved as nonlinear MPEC is formulated. The perfect competition equilibrium model is then transformed into an equivalent mixed integer nonlinear problem.
The solution sets, the practical approaches for solutions, the required techniques to test and to compare the performance of all models are undertaken with calibrated and realistic data. Our results show contracts to integrate renewable energy resources in the system and market-based emissions trading are effective tools for the operation of electricity system. Financial contracts have a strong impact on electricity market outcomes, as risk aversion using these contacts increases generators' profit, which is a counter-intuitive result in our models. Our demand response model shows that end-consumers increase their flexibility thereby maximizing welfare from their utility and cost of electricity consumption. Thus, the demand response model effectively manages price fluctuations and keeps system reliability.
... The global transition to renewable power generation has resulted in significant research efforts to design real-time approaches for power dispatch in power grids [1] and microgrids [2]. Real-time pricing is emerging as a solution for coordinating renewable generation with other intelligent energy resources [3], such as flexible loads [4], battery storages [5] and electric vehicles [6]. Several authors mean real-time pricing when they use the term 'demand response' [7]. ...
Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.
... The objective functions in most DR models encompass economical and technical conditions: quality of service, peak power demand, utility associated with electricity consumption, and discomfort associated with existing consumption patterns (Afşar et al., 2016;Soares et al., 2019;Aussel et al., 2020;Soares et al., 2020;Luo et al., 2020Luo et al., , 1996Ruiz et al., 2018). For instance, an agent-based model that incorporates both balancing and spot power markets is presented in Kühnlenz et al. (2018). A stochastic multi-objective unit commitment real-time DR models with robust optimization and scenario-based stochastic optimization is proposed in Conejo et al. (2010); Alipour et al. (2019). ...
... Maximizes utility of customers given uncertainty. 2) From demand response type (Sioshansi, 2009) (Conejo et al., 2010) (Kühnlenz et al., 2018) Real-time-price based Meets total power and heat demands of consumers without any curtailment. Reduces redispatch costs and loss of load events Integrates consumers flexibility into spot and balancing markets. ...
Demand response (DR) programs have gained much attention during the last three decades to optimize the decisions of the electricity market participants considering the demand-side management (DSM). It can potentially enhance system reliability and manages price volatility by modifying the amount or time of electricity consumption. This paper proposes a novel game-theoretical model accounting for the relationship between retailers (leaders) and consumers (followers) in a dynamic price environment where both players optimize their respective economic goals under uncertainty. The model is solved under two frameworks. First by considering retailer's market power and second by accounting for an equilibrium setting based on a Cournot game. These are formulated in terms of a mathematical program with equilibrium constraints (MPEC) and with a mixed-integer linear program (MILP), respectively. In particular, the retailers' market power model is first formulated as a bi-level optimization problem, and the MPEC is subsequently derived by replacing the consumers' problem (lower level) with its Karush-Kuhn-Tucker (KKT) optimality conditions. In contrast, the Cournot equilibrium model is solved as a MILP by concatenating the retailer's and consumers' KKT optimality conditions. The solution sets, the practical approaches for solutions, the required techniques to test and compare the performance of the model are undertaken with realistic data. Numerical simulations confirm the applicability and effectiveness of the proposed model to explore the interactions of markets power and DR programs. The results confirm that consumers are better off in an equilibrium framework while the retailer increases its expected profit when the market power is considered. However, we show how these results are highly affected by the levels of of consumers' flexibility.
