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Local flexibility markets or smart markets are new tools used to harness regional flexibility for congestion management. In order to benefit from the available flexibility potential for grid-oriented or even grid-supportive applications, complex but efficient and transparent allocation is necessary. This paper proposes a constrained optimization me...
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... At the same time, the separation of the two markets ensures lower matching complexity and higher transparency for the participants [55]. Despite an overall loss of computational efficiency compared to variants that take grid restrictions into account directly in dispatch, downstream flexibility markets have been the subject of practical research projects for several years [56] and can nowadays meet the regulatory requirements in terms of unbundling better than integrated variants. Therefore, the platform follows this approach. ...
Addressing the trends of digitalization, decentralization, democratization, and decarbonization, local peer-to-peer (P2P) markets have the potential to significantly accelerate decarbonization at the communal level. However, due to an increase in the number of energy consumers, such as electric vehicles or heat pumps, grid congestion can occur since actual low-voltage grids are not designed to transmit large loads. This paper introduces a novel concept for a platform to combine the advantages of P2P trading with the need for secure, automated low-voltage grid control, ensuring effective congestion management. Therefore, a dual local energy market has been developed, comprising a P2P energy market and a flexibility market with the latter ensures preventively managing congestion. Furthermore, mechanisms exist to provide curative, real-time congestion management. Additionally, the platform empowers prosumers, i.e. end users that produce and consume electrical energy, with intelligent market strategies to maximize their financial outcomes by participating in both markets. To provide a secure trading mechanism, the novel concept of Self-Sovereign-Identity is integrated into the platform. Tested in a smart grid laboratory at the University of Wuppertal, the platform provides financial gains for prosumers and effectively manages current- and voltage-related grid congestions.
... In reality, all the prosumer information necessary are rarely available, which hinders flexibility estimation being the first step. Even more so, the access to prosumer data is difficult, as there are several obstacles to overcome: 1) data privacy, 2) lack of sensor and system information, and 3) cost for sensor retrofit and energy management systems (You et al., 2021;Zeiselmair and Köppl, 2021). ...
Activation of heat pump flexibilities is a viable solution to support balancing the grid via Demand Side Management measures and fulfill the need for flexibility options. Aggregators as interface between prosumers, distribution system operators and balance responsible parties face the challenge due to data privacy and technical restrictions to transform prosumer information into aggregated available flexibility to enable trading thereof. Thereby, literature lacks a generic, applicable and widely accepted flexibility estimation method for heat pumps, which incorporates reduced sensor and system information, system-and demand-dependent behaviour. In this paper, we adapt and extend a method from literature, by incorporating domain knowledge to overcome reduced sensor and system information. We apply data of five real-world heat pump systems, distinguish operation modes, estimate power and energy flexibility of each single heat pump system, proof transferability of the method, and aggregate the flexibilities available to showcase a small HP pool as a proof of concept.
... Cost-optimized, secure and reliable integration of small-scale demand-side flexibility into DSO congestion management is possible through a market-based approach to local flexibility [32]. LFM is organized as a day-ahead or sometimes as an intraday market, as in some existing pilot projects, since congestion is not always suitable for forecasting a day ahead [33]. ...
... The process of matching supply and demand for flexibility is a core element of LFM. Based on the flexibility bids, the matching of flexibility supply and demand is defined as a multidimensional optimization problem, since the outcome should satisfy the options appropriate for the congestion problem at the lowest cost [32,34]. Cost minimization may refer to the cost of deviation of the energy consumed by the flexible resources from the day-ahead position, where this cost arises from the activity in the balancing market [33]. ...
... The location of flexibility assets and the associated radius of action play an important role in congestion management in the distribution grid [9]. The author in [32] proposes a constrained optimization method to match the flexibility demand of the grid operators with the flexibility supply by using distributed flexibility options in the distribution grid. ...
The economic and technical requirements of current changes in the distribution system are reflected in the use of all available resources and the activation of mechanisms for local use of flexibility. Local flexibility markets are evolving and face numerous obstacles for which appropriate solutions must be found. The local flexibility market will be complemented by the development of a local flexibility register, which will contain all relevant information about the flexibility assets necessary for the efficient operation of the local flexibility market. In this paper, interpretation and quantification of the flexibility sources location on the flexibility service in the distribution grid is given. The information is derived from power flow simulation results and finally written down in the form of line coefficients, which are determined by applying the least squares method to the power flow results. We have developed a Python-based simulator to perform the methodology to determine the information and test it on a realistic medium voltage distribution grid in Bosnia and Herzegovina. This paper confirms the approximate linearity of the active power changes on the demand side to the line load and to the voltage at the nodes for a given operating condition of the distribution grid.
... While the primary purpose of system-wide flexibility services is usually to secure the systemic energy balance in the presence of RG uncertainty, local flexibility is needed to handle location-dependent technical problems introduced by RGs on the distribution grid, such as network congestion and voltage limit violations [12]. The search for the best market design of local flexibility is a field of intense research with several proposals for allocation methods [13] and regulatory reforms [14]. In general, it is expected that local and regional factors lead to significant differences in market design choices as no "one size fits all" solution exists [4]. ...
... Since there are only a few operating local flexibility markets in the experimental phase of development, the simulation of full DSO procurement itself is a challenge. Trading platforms and algorithms suggested in the literature (such as [13]) might be used for this purpose. ...
Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, the optimal market design is not yet known for LF products. This paper investigates a novel cost allocation mechanism in the context of this market challenge. The mechanism is designed to provide several important advantages of peer-to-peer trading without creating barriers to practical application. It provides partial disintermediation. The acquisition of LF remains the responsibility of the DSO, while the financial costs of the transaction are covered on power exchanges (PXs). To provide this functionality, the clearing algorithm of the PX in question has to incorporate a novel feature we call the Payment Redistribution Technique. This technique allows the buyers’ expenses to be larger than the sellers’ income, and the difference is used to finance flexibility costs. Its mathematical formulation is presented and analyzed in detail, considering computational efficiency and accuracy. Afterward, a realistic case study is constructed to demonstrate the operation of the algorithm and its energy market effects.
... The differences between these two options are discussed in more detail in Chapter 4. In the case of FMs, additional information such as sensitivity on the network congestion or restrictions by the flexibility resource is considered in the matching. Therefore, FMs such as ALF [33] or ReFlex [40] use optimisation or respective heuristics with optimal power flow algorithms to assess the ideal flexibility contraction for effectively avoiding grid congestion at the least cost. Schreck et al. [41] integrate such an optimisation-based allocation into a LEM approach to allow the trading of flexibility products such as storage flexibility besides simple sell or buy orders for electricity. ...
... In the case of flexibility trading, nearly all analysed markets choose a discriminatory pricing rule, paying flexibility providers their bidding price. This allows them to consider the non-homogeneous nature of flexibility products [33]. Only Ref. [34] implements a uniform pricing mechanism to determine the final market price. ...
... The authors in Ref. [30] set up a market close to real-time for the trading of energy and flexibility. Zeiselmair et al. [33,34] complement the short-term products with longterm contraction, which can be activated by the system operator on demand. ...
Due to the growing number of Distributed Energy Resources and new electrical loads at the sectoral contact points, novel organisational forms such as Local Energy Markets arise to deal with increasing complexity in the energy system. However, these markets are radically different from traditional energy markets, as they often allow individual prosumers to trade with each other via a peer‐to‐peer scheme. To guarantee tamper‐proof settlement, an increasing number of these markets feature a distributed ledger technology. This paper analyses different design variants of peer‐to‐peer markets, focusing specifically on the allocation mechanism under network constraints as these mechanisms constitute the core component of a market design. We assess these designs concerning user acceptance, economic performance, practicability, and their ability to relieve grid congestion. Further key performance indicators also cover communal revenues or welfare distribution. For this purpose, we developed an agent‐based simulation framework, which builds on data from three German reference municipalities derived from a novel clustering approach. Besides a consolidated presentation of the results, we highlight current implementation obstacles and identify promising concepts for further research.
... In a multi-microgrid system, the idea of an EV aggregator has been employed to deliver electricity in the event of a contingencies with a strong emphasis on thermal safety and the deterioration of the on board lithium-ion battery [16]. In addition, there would be another type of uncertainty that arises during the scheduling of EV due to the varying real-time electricity tariffs and the arrival and departure times of EVs [17]. To cope with this challenge, the energy management of EV parking lots adopt day-ahead scheduling [18], which includes a cancellation penalty for users to relieve the influence on profits caused by the uncertainty when users change their original arrival or departure schedules. ...
... Scheme 3, on the other hand, makes use of intelligence by anticipating future changing grid energy costs and optimizing the capacity usage of EV storage and the PV source. All of these techniques provide for the required power to be accessible from any supply entity at the lowest feasible cost, while also ensuring maximum comfort for energy users by meeting their demands with their immediate energy requests, as indicated in Equation (17). ...
To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%.
... Zeiselmayr and Koppl [5] model their proposed algorithm for using markets to increase flexibility as a constrained optimization matching problem. They include a variety of resources and stakeholders as available options in their optimization, which minimizes the combined costs of positive and negative flexibility subject to a set of operational constraints (e.g., limits on length of call and number of calls per day on a resource) and to the grid operator's demand for flexibility. ...
The rise of intermittent renewable energy generation, the coming mass penetration of electric vehicles and moves to decarbonise the gas grid are leading to widespread innovation experiments within electricity systems and their associated markets[...]
This research investigates the effectiveness of incorporating locational sensitivity factors into local flexibility market clearing mechanisms for effective congestion management and voltage regulation in distribution grids. A centralized local flexibility market optimization model is developed that considers technical and economic constraints. The study aims to explore the requirements for data availability, data quality, and reliable data exchange that can facilitate a broader range of flexibility services, thereby promoting the development of a local flexibility market. Sensitivity factors, including power transfer distribution factors, voltage sensitivity coefficients and transformer sensitivity coefficients, are used to quantify the impact of flexibility asset locations on congestion management and inform clearing rules. These static metrics are insufficient for establishing effective local flexibility market clearing rules when flexibility is procured by distribution system operators. The approach considers the state of the grid when calculating the sensitivity coefficients, which leads to a more accurate evaluation of flexibility bids, especially with regard to the impact of location on congestion management. The proposed mechanism for clearing the local flexibility market assumes continuous communication between the proposed local flexibility market operator and the distribution system operator for dynamic, iterative market clearing, which ensures the protection of grid data and a more accurate bid evaluation. The study demonstrates that the inclusion of locational information significantly increases the effectiveness of the proposed local flexibility market-based congestion management. The developed simulator for the proposed local flexibility market provides valuable insights into the interaction between the proposed local flexibility market and the distribution grid. The research results, derived from selected use cases, emphasize the importance of location-based sensitivity factors in the proposed local flexibility market clearing for distribution grids. The proposed approach offers a promising solution for optimizing congestion management and voltage regulation while ensuring efficient integration of distributed energy resources into distribution grids.
As the number of distributed energy resources are being rapidly grown in the distribution network, many discussions over local markets to coordinate these emerging technologies are flown around. Thus, this chapter aims to investigate the concept of local energy markets (LEMs) from several perspectives. The concept of LEMs is first introduced, then the benefits and challenges of LEMs implementation are investigated. Since, LEM should be merged into existing market structure, the interaction among two market structures is discussed. As Blockchain-based LEM is drawn enough attention nowadays, the benefits, and challenges of such implementation are covered and eventually some LEM projects around the globe are introduced. We have collected the gaps to be bridged in the future in the concept of LEM at the end of this chapter.
