Conference Paper

Prosumer Integration in Flexibility Markets: A Bid Development and Pricing Model

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The impact of renewable energies on the power grid is continuously increasing. Besides the emission-free power generation, the renewable energies often are the cause for congested grids, component failure and costly interventions by the distribution system operators (DSO) and transmission system operators (TSO) in order to maintain grid stability. The scientific community discusses in recent years the usability of distributed energy resources (DER) as flexible devices. However, no approach can be found that actually quantifies the potential flexibility and sets a price to it. The model presented in this paper optimizes the charging operation of an electric vehicle (EV) according to a price signal with a state of the art exhaustive search algorithm. Furthermore, this model offers all possible deviations from the optimal operation as flexibility to a corresponding market platform and sets a price to each offer, which is dependent on the future price level of the energy. With this model, it is possible to offer positive and negative prices for flexibility. The proposed model shows that an exhaustive enumera-tion algorithm is feasible to calculate flexibility offers, prices and applicable on currently discussed platform models. The example of an EV charging schedule is successfully modelled and described in this paper.

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... While regulation and various market designs for energy trading already exist, academic and industrial research now focus on introducing unique flexibility platforms [6]. Such new platforms will allow residential consumers and prosumers to participate with their distributed energy resources (DER)-such as combined-heat-and-power units (CHP), electric vehicles (EV), residential heat pumps (HP), photovoltaic systems (PV), and battery storage units-as well as large industrial parties to offer flexibility [7][8][9]. In the future, SO will be able to manage grid congestions in a less resource-intensive manner and potentially avoid costly grid expansions and the curtailment of VRE [10,11]. ...
... Zade et al. published an HEMS model that optimizes the charging process of an electric vehicle (EV), and calculates the flexibility based on synthetic electricity prices, vehicle availabilities, and energy demands [9]. In order to analyze the realistic flexibility potential of EVs in a distribution grid, this paper describes a detailed case study conducted with vehicle field trial data from California, USA and Germany, three electricity tariffs, two controller strategies, and three charging power levels. ...
... However, this paper focuses on the quantification of flexibility of EVs and therefore the pricing is excluded from this analysis. Nevertheless, one possible pricing mechanism for flexibility of EV is described in [9]. ...
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The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs.
... As a result, energy production becomes volatile, leading to varying energy prices depending on the actual energy availability. To solve this problem, some typical solutions include grid expansion, modulation of large power plants and especially curtailing renewables [1,2]. In recent years, a lot of literature propose flexibility of distributed energy source as an alternative to the traditional solutions. ...
... Flexibility is described by the Union of the Electricity Industry (Eurelectric) as "the modification of the generation injection and/or consumption patterns in reaction to an external signal (price signal or activation) in order to provide a service within the energy system" [3]. The transmission system operators (TSO) and distribution system operators (DSO) can use flexibility to maintain grid frequency and avoid grid congestion [2]. ...
... In a former work of flexibility calculation for electric vehicle (EV) the author has chosen the cheapest prices available in 24 hours to charge the EV and optimize the operation of EV to reach the cost-optimal solution [2]. But finding the optimal operation of HP or CHP is more complex because its heating power depends on several boundary conditions, e.g. the weather conditions and charge of state (SoC) of the heat storage. ...
... Some studies have claimed for a direct interaction of the prosumer with the market platform and some studies have analyzed the need for the market players like aggregators to pool the flexibility options [7]- [11]. Only a few publications have discussed methods to calculate the amount of flexibility that a household prosumer can offer [1], [12]. This paper presents a flexibility model, which is used to calculate the amount of flexibility that a prosumer can offer with photovoltaics (PV) and battery storage systems. ...
... The EMS performs three main tasks (see Fig. 1); cost optimal optimization, flexibility potential identification with offers generation and re-optimization once an offer is selected. The flexibility offers of this model have similar characteristics and are compatible with the flexibility platform used in [12]. ...
... Flexibility of a device is defined as the technically feasible deviations from the optimal operation schedule [12]. Negative flexibilities are deviations, which feed-in additional energy to support the grid stability, while positive flexibilities reduce the grid burden either by energy withdrawal or by curtailing scheduled grid feed-in. ...
Conference Paper
Increasing integration of renewables can cause congestion and power grid instability. Recent congestion management studies have shown the possibilities to offer flexibility with distributed energy resources (DERs) and the need for flexibility market platforms. This paper presents a flexibility model for a combination of photovoltaics and battery storage system. This model takes different forecast data as inputs and performs an optimization to obtain a cost optimal operational plan. Any deviation that a DER could offer from this optimal plan is considered as its flexibility potential. The model outputs a list of flexibility offers. In addition, this paper details the different possibilities of offering positive and negative flexibility with a novel offer pricing strategy. The model re-optimizes itself to a new optimal operation plan once a desired flexibility bid is chosen. Two case studies are discussed in this paper to substantiate the successful implementation.
... On the contrary, negative flexibility is needed when demand exceeds generation. It is possible via discharging energy storage systems or reducing the needs of the demand side [13,14]. ...
... Households, which have several DERs, are connected to this platform and the energy market. The flexibility platform receives flexibility offers from the households as well as flexibility needs by demanders such as TSOs and DSOs [14]. Additionally, several projects in Europe have been initiated to explore flexibility capabilities of prosumers, Such as EMPOWER, Flex4Grid, P2P-SmartTest, and GOFLEX [22]. ...
... Thus, it is not preferred [27]. The authors in [14] have used the brute-force algorithm to optimize the charging process of an electric vehicle in order to determine its flexibility potential. ...
Beside their advantageous consequences, the growth of renewable energy systems and distribution energy resources, especially on small-scale levels, has led to negative impacts on several aspects. One major disadvantage is their unfavorable influence on operating the electricity grid, for example, causing grid congestions or instabilities. In this thesis, an energy management system is developed to propose a solution to these problems through the concept of flexibility, which concerns the alteration of electricity production or demand. Using several forecasted inputs, the system first optimizes the usage of the grid, a photovoltaic system and a battery to supply the electric load of a prosumer for a short-term period. Based on the produced optimal operation plan, the flexibility potential of the photovoltaic system and the battery is determined in terms of starting time, power, energy and price, and then sent as a list of offers to a market platform where flexibility trading takes place. Upon a flexibility call, the operation plan deviates from optimality, which requires the system to perform the optimization task once more to produce a new optimal operation plan. Strategies to identify and price flexibility offers are discussed thoroughly in this thesis. Representative examples of operation plans with flexibility calls for various cases are also presented. Finally, sensitivity analyses are conducted to study the effect of modifying the size of concerned units and the input prices on the characteristics of flexibility offers.
... Determining flexibility as feasible deviations from a previously determined optimal schedule is another approach for flexibility quantification found in the literature. For example, [31] optimize charging cycles for electric vehicles and formulate bids for flexibility based on feasible delayed or advanced charging compared to scheduled charging. ...
... Different from other approaches for quantifying flexibility, such as [27,31], no (optimal) reference schedule needs to be determined for the flexibility calculation, but any operation between two extreme (minimum and maximum) schedules is possible. The calculation of this set of feasible schedules, or operational zone as we refer to it here, does not require computationally expensive machine learning approaches as used in [12,18]. ...
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Heat generation that is coupled with electricity usage, like combined heat and power generators or heat pumps, can provide operational flexibility to the electricity sector. In order to make use of this in an optimized way, the flexibility that can be provided by such plants needs to be properly quantified. This paper proposes a method for quantifying the flexibility provided through a cluster of such heat generators. It takes into account minimum operational time and minimum down-time of heat generating units. Flexibility is defined here as the time period over which plant operation can be either delayed or forced into operation, thus providing upward or downward regulation to the power system on demand. Results for one case study show that a cluster of several smaller heat generation units does not provide much more delayed operation flexibility than one large unit with the same power, while it more than doubles the forced operation flexibility. Considering minimum operational time and minimum down-time of the units considerably limits the available forced and delayed operation flexibility, especially in the case of one large unit.
... First, the local approach is addressed by Refs. [95,96] where flexibility is seen as a local resource with high spatial resolution. Generally, both flexibility suppliers and buyers are traded in a local environment, with the mediation of aggregators, DSO, TSO, or even local system operators. ...
Flexibility has emerged as an optimal solution to the increasing uncertainty in power systems produced by the continuous development and penetration of distributed generation based on renewable energy. Many studies have shown the benefits for system operators and stakeholders of diverse ancillary services derived from demand-side flexibility. Cost-benefit analysis on these flexibility services should be carried out to determine the profitable applications, as well as the required adjustments on energy market, price schemes and normative framework to maximize the positive impacts of the available flexibility. This paper endeavors to review the main topics, variables and indexes related to the profitability analysis on demand-side flexibility, as well as the influence of energy markets, pricing and standards on revenue maximization. The conclusions drawn from this review demonstrate that the profitability of flexibility services considerably de-pends on energy market structure, involved assets, electricity prices and current ancillary services remuneration.
... The authors in [17] and [18] have used the proposed platform. The HEMS in these works, firstly finds an optimal strategy for the prosumer. ...
Conference Paper
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Providing sufficient flexibility in modern power systems has become an unavoidable feature of sustainable energy systems. This flexibility should be supplied by different sectors and players of power systems. The role of demand especially prosumers in providing flexibility have recently been highlighted. In this regard, this paper presents a new flexibility-based algorithm for a Home Energy Management System (HEMS). The HEMS focuses on Electric Vehicles (EVs) and Electrical Energy Storage (EES) as main sources of flexibility products. Two main products, namely, positive and negative flexibility are defined as deviations from optimal energy schedules attained by the HEMS. Each flexibility’ offer related to the device is offered at a specific price in the flexibility platform. Final flexibility plans are extracted after running a linear optimization model. It has been shown that these products can properly enhance the flexibility level of distribution systems and simultaneously impose no additional cost for prosumers.
... In contrast, this work takes into consideration different household types and human behavior, which allows conducting a sensitivity analysis of aggregated flexibility from multiple households using different ratios of available system components. Compared to other works that mostly provide a linear flexibility representation [29,39], this work uses a three-dimensional riverbed flexibility representation. This representation provides a clear and understandable way to analyze the dynamics of the flexibility quantity and its cost, and allows to observe its influencing factors in detail. ...
Flexible household devices, such as heat pumps combined with thermal energy storage or battery energy storage units, can provide flexibility to the electricity sector. However, to make flexibility available to the market, it has to be correctly quantified, and its cost has to be estimated. In this work, a methodology for generic flexibility quantification is proposed and developed in a Python environment using model predictive control. The chosen methodology allows to quantify the adjustable power, and also to determine the corresponding cost of the flexibility provision. It was observed that the available flexibility and its cost is influenced by many factors such as system components, human behavior, building thermal parameters, and price signals. Also, the inclusion of even a low share of households with batteries or electric vehicles smoothens the aggregated flexibility profile, and a considerable amount of flexibility is available at almost any point in time.
End-user flexibility is an essential resource for decarbonized energy systems, and can be exploited via distributed flexibility markets. To participate, end-users must weigh potential revenues against their primary objectives, subject to their operational constraints. This work presents a technology-neutral framework to simultaneously determine the optimal operation, flexibility bids and prices by extending original operation planning problems into two-stage stochastic ones. Recovery constraints are introduced to exclude potentially adverse bids. As case studies, diverse residential end-users, who minimize their cost and additionally place flexibility bids, are analyzed. The results reveal complex time-varying and system-specific dynamics of bid volumes and prices. For example, heating systems receiving flat electricity rates can effortlessly bid flexibility from forcing early heat generation, namely, positive flexibility for combined heat and power units and negative flexibility for heat pumps. However, under time-varying rates, bid patterns change and become synchronized, which can cause scarcity in markets with homogeneous participants. With this framework, end-users can assess technical and economic potentials, plan their operations and market participation. As operation planning also reckons with the expected flexibility demand of the system, bids in time of need are likely higher. Lastly, detailed potential assessments also aid operators in designing flexibility portfolios or markets.
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This paper presents a general description of local flexibility markets as a marketbased management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids' hosting capacity.
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The increasing penetration of distributed energy resources in the distribution grid is producing an ever-heightening interest in the use of the flexibility on offer by said distributed resources as an enhancement for the distribution grid operator. This paper proposes an optimization problem which enables satisfaction of distribution system operator requests on flexibility. This is a decision-making problem for a new aggregator type called Smart Energy Service Provider (SESP) to schedule flexible energy resources. This aggregator operates a local electricity market with high penetration of distributed energy resources. The optimization operation problem of SESP is formulated as an MILP problem and its performance has been tested by means of the simulation of test cases in a local market. The novel problem has also been validated in a microgrid laboratory with emulated loads and generation units. The performed tests produced positive results and proved the effectiveness of the proposed solution.
Conference Paper
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Different optimization tools have been developed to find the best trade-off between competitive goals. The optimization problem is typical of the design process, where different design solutions have to be compared to achieve one or more objectives, often in contrast with each other. A quite novel application of optimization is building energy model calibration. The use of well-calibrated energy simulation models is key for successful buildings’ retrofit or operation management and the optimization techniques can improve the reliability of the results. The typical optimization method consists in the analysis of all the alternatives’ performances, developing a full factorial plan and simulating all the possible options (brute-force approach). However, this process could take unsustainable long time. That is why some optimization tools, based on evolutionary algorithms have been developed to speed up the process. This study compares results obtained through the brute-force approach and the evolutionary optimization methods applied on the calibration of a large educational building model located in the province of Treviso, north of Italy. The total design space consists of about 72 000 EnergyPlus building models. Two optimization-based calibrations have been repeated using a genetic algorithm by means of jEPlus+EA on a local computer and through parametric simulations implemented by jEPlus on a cloud service. The quality of results from the evolutionary optimization tools as compared to a full parametric study applied on calibration have been discussed. Scenarios of applicability are drafted. On a practical level, the research is a contribution for the selection of methods and tools for the preparation of models that can lead to optimized retrofit interventions and rationalization of building management and operation.
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In many electric systems worldwide the penetration of Distributed Energy Resources (DER) at the distribution levels is increasing. This penetration brings in different challenges for electricity system management; however if the flexibility of those DER is well managed opportunities arise for coordination. At high voltage levels under responsibility of the system operator, trading mechanisms like contracts for ancillary services and balancing markets provide opportunities for economic efficient supply of system flexibility services. In a situation with smart metering and real-time management of distribution networks, similar arrangements could be enabled for medium-and low-voltage levels. This paper presents a review and classification of existing DER as flexibility providers and a breakdown of trading platforms for DER flexibility in electricity markets.
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Future electric power systems will face new operational challenges due to the high penetration of distributed energy resources (DERs). In Denmark distribution system operator (DSO) expects a significant congestion increased in distribution grids. In order to manage these congestions and mobilize the DERs as economically efficient as possible in the future distribution grid, the brand new notion of Flexibility Clearing House (FLECH) is proposed in this paper. With the Aggregator-based offers, the proposed FLECH market has the ability to promote small scale DERs (up to 5MW) for actively participating in trading flexibility services, which are stipulated accommodating the various requirements of DSO. Accordingly, the trading setups and processes of the FLECH market are also illustrated in detail. A quantitative example is utilized to illustrate the formulation and classification of flexibility services provided by the DERs in the proposed FLECH market.
Conference Paper
This paper introduces a decentralized implicit interaction framework for trading flexibility available from proactive end users (prosumers) in an economically-efficient way. The proposed framework consists of two mechanisms: ahead planning via markets and real-time dispatching. The ahead, market-based planning includes two mechanisms, day-ahead and intra-day, which are operated by a local flexibility market operator. Each local market seeks to adjust the energy programs before they will be forwarded to the wholesale energy market such that, if accepted, the programs will result in no congestion issues in the distribution grid. The real-time dispatching consists of a set of control actions that are determined and implemented by the DSO to resolve a network congestion issue, should the market-based planning fail. The second part of the paper focuses on establishing a strategy for the DSO to procure the flexibility it needs from the above-mentioned day-ahead and intra-day markets, as well as through real-time dispatching, at the lowest possible cost. To this end, a hierarchical, bi-level optimization problem is proposed, which is mathematically proven to yield the optimal bidding strategy (including prices and volumes) for the DSO.
In the past few decades, restructuring has overtaken all possible domains including the electric supply industry. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated one. Such a competitive market has paved way for innumerable participants. This has led to overloading and congestion of transmission lines. Moreover, open access transmission network has ingenerated a more intensified problem of congestion. Thus, congestion management in power systems is germane and of central importance to the power industry. In this paper a review work is carried out to unite all the publications in congestion management.
This paper concerns the problem of optimally scheduling a set of appliances at the end-user premises. In the context of electricity smart grids, the electric energy fee varies over time and the user may receive a reward from an energy aggregator if he/she modifies his/her consumption profile during certain time intervals. The problem is to schedule the operation of the appliances taking into account overall costs, climatic comfort level and timeliness. We devise a MILP model and a heuristic algorithm accounting for a typical household user. Several numerical simulation results are reported, showing that the problem can be efficiently solved in real-life instances.
This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.
In this paper, a method which employs a Modified Teaching–Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Generation (DG) units in distribution systems. For the sake of clarity, and without loss of generality, the objective function considered is to minimize total electrical power losses, although the problem can be easily configured as multi-objective (other objective functions can be considered at the same time), where the optimal location of DG systems, along with their sizes, are simultaneously obtained. The optimal DG site and size problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used by researchers to solve this problem because of their independence from type of the objective function and constraints. Recently, a new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations. A comparison between the proposed algorithm and a brute force method is performed. Besides this, it has also been carried out a comparison using several results available in other articles published by others authors. Numerical results for two test distribution systems have been presented in order to show the effectiveness of the proposed approach.
This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization.
In this paper, the major benefits and challenges of electricity demand side management (DSM) are discussed in the context of the UK electricity system. The relatively low utilisation of generation and networks (of about 50%) means that there is significant scope for DSM to contribute to increasing the efficiency of the system investment. The importance of the diversity of electricity load is discussed and the negative effects of DSM on load diversity illustrated. Ageing assets, the growth in renewable and other low-carbon generation technologies and advances in information and communication technologies are identified as major additional drivers that could lead to wider applications of DSM in the medium term. Potential benefits of DSM are discussed in the context of generation and of transmission and distribution networks. The provision of back-up capacity by generation may not be efficient as it will be needed relatively infrequently, and DSM may be better placed to support security. We also present an analysis of the value of DSM in balancing generation and demand in a future UK electricity system with significant variable renewable generation. We give a number of reasons for the relatively slow uptake of DSM, particularly in the residential, commercial and small business sectors. They include a lack of metering, information and communication infrastructure, lack of understanding of the benefits of DSM, problems with the competitiveness of DSM when compared with traditional approaches, an increase in the complexity of system operation and inappropriate market incentives.
Congestion management is one of the major tasks performed by system operators (SOs) to ensure the operation of transmission system within operating limits. In the emerging electric power market, the congestion management becomes extremely important and it can impose a barrier to the electricity trading. This paper presents papers/literature on congestion management issues in the deregulated electricity markets. There are 211 citations referenced in this bibliography. The general electronic web sites and the web sites dealing with the issue of congestion management are also listed.
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