Flexible Consumption Research Articles

Dispatch of Decentralized Energy Systems using Artificial Neural Networks: A Comparative Analysis with Emphasis on Training Methods

Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

A step closer towards active and flexible consumers: enhancing involvement through energy consumption feedback

The level of consumer awareness and comprehension, during a period of abundant energy services, has a crucial role in their initial adoption and later engagement with the services. Digital energy platforms bring the ≫intangible electricity≪ closer to people’s daily lives and encourage new ways of thinking about and consuming energy. Energy feedback connects consumer’s consumption to their daily activities and provides insights. It enables them to learn about the consumption, learn how to reduce or optimise it and later, learn how to be flexible when needed. Besides investigating how familiar are the Slovenian households with their own consumption and energy related behaviour, the goal of this paper is also to investigate their preferences in visualizing their electricity consumption. The consumption estimation was validated on real case study using data from 642 Slovenian households. Results show that consumers that use electricity for space and water heating, underestimated their consumption up to 22.5%. A categorization of households based on their water and space heating systems is also presented.

Indicators for suitability and feasibility assessment of flexible energy resources

Recommender systems play a critical role in optimizing building energy consumption by providing personalized advice based on data analytics and user preferences. However, the literature highlights the need for systems that can justify their recommendations, as many of these systems use non-transparent machine-learning techniques. This research introduces two distinct types of indicators with three main goals: to identify patterns of flexible consumption behavior using transparent and straightforward methods suitable for remote decision support systems, thereby eliminating the need for extensive databases; to evaluate the feasibility of installing solar panels on building facades, rooftops, and structures using high-resolution 3D models; and to enhance understanding through a quantitative assessment of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability through the Flexible Consumption Indicators. Topology Indicators perform a quantitative assessment of the feasibility of support surfaces for installing photovoltaic panels, taking into account rooftop pitch angles, orientations, and surrounding and internal structures, identifying those areas exposed to sufficient levels of irradiation. This study, which uses actual consumption profiles and similar households' buildings 3D models, demonstrates how the proposed indicators can aid identifying users with flexible consumption profiles that reside in buildings compatible with renewable energy sources, aiding in decision-making process within the energy transition.

Shifting patterns – The patterned enactments of flexible electricity consumption by Norwegian households

Based on semi-structured interviews with 20 households, this study explores how households interpret their possibilities to time shift their electricity consumption. Drawing from the concept of flexibility capital (Powells and Fell, 2019), we illustrate the patterned nature of flexibility work. We complement this with an analysis of how people enact time shifting in micronetworks that include people, artifacts, infrastructure, knowledge, and institutions (Sørensen et al., 2000). We identify four types of micronetworks that show that households interpret and domesticate flexible consumption in a variety of ways. First of all, most households with high flexibility potential did not see their advanced energy technologies as useful for time shifting consumption and thought that time shifting is meant to be done by others in society. Meanwhile, many households with lower flexibility potential found time shifting consumption meaningful, interpreting it as rescheduling dishwashing and laundry. The findings imply that there is a need to account for whether households view the technologies they possess as useful for flexibility work; in other words, whether these technologies are understood and enacted as flexibility capital. The study discusses the possibility that many households with high flexibility potential may be indifferent to price incentives for time shifting. Additionally, the findings highlight that households are actively constructing the situated meaningfulness of time shifting. It is important that professional communities acknowledge the variety of interpretations of what time shifting is about instead of seeing these as just overflows of the dominant smart grid logic.

Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.

Environmental and economic dispatching strategy for power system with the complementary combination of wind-solar-hydro-thermal-storage multiple sources

The linkage, coordination, and complementary cooperation of energy supply can improve the efficiency of transportation and utilization. At present, the level of new energy consumption needs to be improved, the coordination of the source network load storage link is insufficient, and the insufficient complementarity of various types of power sources in the power system. This article fully explores the differences and complementarities of various types of wind-solar-hydro-thermal-storage power sources, a hierarchical environmental and economic dispatch model for the power system has been established. Among them, the upper level model takes the flexible consumption of new energy as the optimization goal, the middle level model uses a combination of hydropower station and energy storage to minimizes the fluctuation variance and peak-to-valley difference of the net load curve, the lower level model aims to achieve optimal environmental and economic benefits of the power system, comprehensively considers the coal consumption cost, startup and shutdown cost, energy storage operation cost, and pollutant emissions of thermal power units, determines the startup and shutdown mode and output power of thermal power units. Finally, an improved IEEE 6-machine 30-node system is used as an example for simulation analysis, the results show that after applying the proposed hierarchical environmental and economic dispatch strategy of the power system, the fluctuation variance and the peak-to-valley difference of the net load curve have been reduced by 46.3% and 31.5%, respectively, and the environmental and economic benefits of the system is improved by 5.1% compared with the traditional economic dispatch strategy. It can meet the requirements of energy system cleaning and decarbonization while improving the operation economy, which verifies the effectiveness of the proposed environmental economic dispatch model.

On the Impact of Flexibility on Demand-Side Management: Understanding the Need for Consumer-Oriented Demand Response Programs

While demand response programs inherently depend on consumer acceptance in order to be successful, consumer behavior is often overlooked when designing such programs. This paper addresses the impact of consumer flexibility in terms of appliance use on the success of a demand response program, measured through the overall grid stability assessed by the demand’s peak-to-average ratio. We employ a bootstrapping approach to simulate energy communities from real-life consumer data and implement a state-of-the-art demand response system. Results suggest that higher consumer flexibility under real-time energy tariffs implies higher degrees of grid stability, with real-time pricing decreasing the average peak-to-average ratio by 4.6% compared to time-of-use tariff and with highly flexible consumers showing a 23% lower peak-to-average ratio than regular consumers on average. Yet, it is possible for higher flexibility to be detrimental to grid stability by increasing the peak-to-average ratio under a time-of-use tariff. This result highlights the importance of understanding the interplay between different factors that influence energy consumer behavior, a research stream that has been under investigated thus far.

A comprehensive survey of low‐carbon planning and operation of electricity, hydrogen fuel, and transportation networks

AbstractThe trend of global energy systems towards carbon neutrality has led to an escalating interdependency between electricity, hydrogen fuel, and transportation networks. However, the means of surmounting the many challenges confronting the optimal coupling and coordination of electric power, hydrogen fuel, and transportation systems are not sufficiently understood to guide modern infrastructure planning operations. The present work addresses this issue by surveying the extant literature, relevant government policies, and future development trends to evaluate the present state of technology available for coordinating these systems and then determine the most pressing issues that remain to be addressed to facilitate future trends. On the one hand, the users of transportation networks represent flexible consumers of electric power and hydrogen fuel for those connected via devices such as electric vehicles and hydrogen fuel cell vehicles through charging stations and hydrogen refuelling stations. On the other hand, power grids can mitigate the negative effect of random charging behaviours on grid security through time‐of‐use electricity pricing, while excess renewable energy outputs can be applied to generate hydrogen fuel. The findings of this overview offer support for infrastructure planning and operations. Finally, the most urgent issues requiring further research are summarised.

Unleashing the economic and ecological potential of energy flexibility: Attractiveness of real-time electricity tariffs in energy crises

The European energy crisis in 2021 and 2022 emphasized the importance of energy flexibility to mitigate price peaks and manage increased market volatility. Dynamic electricity tariffs are key to unlocking the potential of energy flexibility, as they incentivize flexible consumers to reduce their costs by shifting their load to periods of low prices. We quantify the potential of dynamic tariffs and focus on their economic and ecological potential particularly in energy crises. Using German Day-Ahead spot market data covering 2019 to 2022 as basis for a dynamic tariff, we determine the cost and emission spread between non-flexible and flexible industrial processes. Our results show that energy flexibility together with the real-time electricity tariff lead to energy cost reductions, with relative cost reductions of flexible loads being up to 12 times higher in the energy crisis. Moreover, pre-crisis electricity costs and associated emissions were highly positively correlated, implying flexibilities in real-time electricity tariffs may minimize electricity costs while simultaneously reducing emissions. Based on our results, we conclude that real-time electricity pricing provides a suitable instrument to (1) incentivize necessary investments in energy flexibility, especially in energy crises, and (2) facilitate flexible consumers to reduce costs and emissions at the same time.

РОЛЬ УПРАВЛІННЯ ДИСТРИБУЦІЙНИМИ КАНАЛАМИ ГОТЕЛЮ ПРИ ОВЕРБУКІНГУ

The application of the overbooking process for different segments of the hotel services market requires the study of factors and prerequisites for influencing demand. The main problem faced by hotels when using overbooking is that, despite a set of booking requests that exceeds the total number of rooms, there is a lack of room capacity in the intervals between bookings. Hotel owners and managers, including those with a limited capacity, are increasingly interested in revenue management methods, but not everyone has managed to implement a system of Revenue Management, which is an integral part of effective management. On the one hand, a revenuemanager can optimize profitability and profitability not only from room sales, but also from other hotel revenue centers; on the other hand, knowing (analyzing) the habits of regular guests, it is possible to increase their loyalty by offering a personalized approach to providing special conditions for hotel services. Determining the potential of bookings and their impact on the total revenue of a hotel is based on an arsenal of data on needs in the general economic aspect, which form the interests, expectations, and priorities of consumers in choosing a hotel service. From the perspective of market relations, it is the needs of consumers that are the basis for the formation of consumer demand for hotel services. In the practical context of distribution of hotel services, the main dispute in the formation of a room reservation guarantee system is reflected in the content of compensation processes for reasons of no-show or early departure. In accordance with this statement, the fundamental content, prerequisites and benefits of using overbooking for the guest are the flexible consumption, at the level of the hotel enterprise – it is to ensure the process of managing the occupancy as a set of separate areas of planning and maximizing revenues. However, it is worth noting that the increasing application of overbooking depends on individual decisions of the hotel company and requires differentiation of the hotel service, which, in particular, involves expanding the price plan, introducing new methods in the process of their promotion and transforming distribution. The problem is that the level of overbooking is determined primarily by the method of trial and error and can lead either to resale of rooms and forced relocation of guests or to an unreasonably large quantity of vacant rooms and a lack of revenue.

Towards a low-carbon and low-risk community: Risk-constrained optimization of energy pricing in electricity and gas networks with flexible carbon-responsive loads

This paper proposes carbon-based nodal pricing, which utilizes carbon-tracing analysis in its structure. The carbon-tracing algorithms enable the independent system operator (ISO) to better manage and establish a fair pricing mechanism, considering the actual contribution of each plant to the network's total daily emissions. The ISO, by utilizing carbon-tracing analysis, can easily compute nodal carbon intensity and branchial carbon flow, which are used in nodal pricing analysis. Furthermore, in this paper, for motivating flexible consumers, especially those who have higher impacts on carbon emissions, a carbon-based incentive framework called the flexible carbon-responsive (FCR) program is established, which helps the consumers control their carbon output and enables the ISO to decrease the network's total carbon emissions. In the proposed structure, the worst-case scenarios, which arise from uncertain parameters, can easily affect the simulation results. As a result, in order to avoid such issues, analyzing risks associated with uncertainty is the next focus of this research, and the downside risk constraints (DRC) approach is used to accomplish so. The DRC technique helps the ISO achieve an appropriate balance between the cost associated with risk and the anticipated cost, with the simultaneous objective of minimizing overall risk. Last, a modified IEEE 24-bus transmission system and the IEEE 20-bus gas network are utilized to evaluate the suggested carbon-based pricing mechanism. The simulation results show that applying the FCR program and carbon-based pricing causes the total operation costs to decrease by 100%, resulting in an increase of only 0.71% under the proposed DRC method. It could be noted that the total operation cost increment in the with-carbon tax case is 0.18% lower compared to the no-carbon tax policy.

Day-ahead optimal dispatch of a virtual power plant in the joint energy-reserve-carbon market

As an efficient technique to manage massive distributed energy resources (DER), the virtual power plant (VPP) can leverage the aggregated flexibility to enhance the economy and security of the system. However, few efforts have been reported on the carbon emission reduction benefit of the VPP. In this paper, a novel incentive mechanism for low-carbon demand response is designed, which is based on dynamic pricing and carbon intensity. Considering the uncertainty of renewable energy output characterized by the chance-constrained approach, a coordinated optimization model in the joint day-ahead energy, reserve and carbon market for the VPP operator and flexible consumers is proposed, including the pre-dispatch model, the energy storage systems (ESSs) incorporated carbon emission flow calculation model, and the bi-level final-dispatch model. Numerical simulations of both small-scale and large-scale cases demonstrate that the proposed low-carbon dispatch strategy can boost the payoff of the VPP operator, reduce carbon emissions in the joint energy-reserve-carbon market, and improve the robustness of the power system against uncertainty.

Optimal operation of energy hub considering reward-punishment ladder carbon trading and electrothermal demand coupling

As the massive carbon emissions from the power system, the shift of the power system to low-carbon becomes inevitable. In this context, this paper proposes a Stackelberg game approach to develop the operational strategy of energy hubs, which considers the reward and punishment ladder carbon trading mechanism and the coupling of electrothermal demand. To avoid the fixed carbon price in the past and the neglect of the incentive space for carbon trading, a ladder carbon trading mechanism of rewards and punishments is introduced to effectively depict carbon trading costs. The electrothermal coupling of the demand side is incorporated into the Stackelberg game model, and the flexible consumption model of users participating in the integrated demand response is established. The above model is solved by combining heuristic algorithm with solver CPLEX, which can solve the data privacy leakage problem of both parties. Simulation results show that the strategy can bring economic benefits to energy hub operators and users. Carbon emissions across the system were reduced by 14.4 %. Users with electric heating equipment can fully improve the electric load and heat load adjustment level, and improve their economic benefits by 2.4 % at the cost of a 3.2 % increase in carbon emissions.

Flexible Operation to Reduce Greenhouse Gas Emissions along the Cold Chain for Chilling, Storage, and Transportation—A Case Study for Dairy Products

The further expansion of renewable energies in Germany requires flexible consumers to balance fluctuations in electricity production from variable renewable energies. Cold storage warehouses, due to their inherent storage capacity and widespread use, are well-suited for integrating more renewable energies. The potential of cold storage warehouses is often viewed in isolation and not in conjunction with the cold chain’s upstream and downstream processes. By adjusting the temperatures within the processes, the individual links in the cold chain can be made flexible. To assess the effects of flexibilization on emissions and electricity costs, thermodynamic models of the individual links in the cold chain and of a yogurt pallet are developed and linked together. Due to temperature fluctuations in the products resulting from the flexibilization, emission evaluations must be considered throughout the cold chain. Results of the simulation for the study period show that emissions reductions and electricity cost savings can be achieved in all three links when they are made flexible. However, the savings vary in magnitude. Only minor savings can be achieved in the cooling tunnel. The greatest potential for savings is in refrigerated transport, if deeper cooling occurs in the process before, i.e., in the cold storage warehouse.

Analyzing the role of consumer behavior in coping with intermittent supply in water distribution systems

Abstract A substantial number of water distribution systems (WDS) worldwide are operated as intermittent water supply (IWS) systems, delivering water to consumers in irregular and unreliable manners. The IWS consumers commonly adapt to flexible consumption behaviors characterized by storing the limited water available during shorter supply periods in intermediate storage facilities for subsequent usage during more extended nonsupply periods. Nevertheless, the impacts of such consumer behavior on the performance of IWS systems have not been adequately addressed. Toward this direction, this article presents a novel open-source Python-based simulation tool (EPyT-IWS) for WDS, virtually acting like an IWS modeling extension of EPANET 2.2. The applicability of EPyT-IWS was demonstrated by conducting hydraulic simulations of a typical WDS with representative IWS attributes. Different IWS operation cases were considered by varying the amount and consistency of the water availability to the consumers. EPyT-IWS outputs showed that domestic storage of water within underground tanks and subsequent pumping into overhead tanks allows consumers to cope with the intermittent water availability and suitably meet their demands. Besides the interval, the clock time of the water supply was predicted to influence IWS consumers’ ability to meet water demands.

Personalized retail pricing design for smart metering consumers in electricity market

In the current deregulated electricity market, flexible consumers are more active in participating in market activities via the representation of electricity retailers. However, without an effective communication infrastructure, the connection between retailers and the consumers they serve is incomplete. Nowadays, smart meters are being rolled out worldwide to enhance the connection and data exchanges between retailers and consumers. Specifically, smart meters enable retailers to provide customers with detailed information about retail tariffs and their energy usage at different times of the day, which in turn enables customers to manage their energy use more proactively. This paper drops this assumption and makes use of data acquired from smart meters to design a personalized retail pricing scheme for different types of consumers. To formulate this problem, a bi-level optimization model is proposed, with the upper-level problem representing the pricing decision made by the retailer and two lower-level problems representing the demand response of consumers and the wholesale market clearing process, respectively. Afterward, we convert this bi-level optimization model into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker optimality conditions and complementary conditions. The scope of the examined case studies is fourfold. First, consumers are classified based on their daily load profiles using the advanced clustering method. Second, the physical benefit of fully exploring the consumer’s demand flexibility as well as the economic benefits of increasing retailers’ profitability and reducing consumers’ energy bills are evaluated with respect to the traditional uniform retail pricing scheme. Third, the impacts of consumers’ demand flexibility on electricity market outcomes and business cases are investigated. Finally, the proposed personalized retail pricing scheme is verified to relieve the strategic retailer’s market power reduction caused by the flexibility of demand, which is beneficial to the retailer’s profitability.

Optimal Dynamic Mechanism Design With Stochastic Supply and Flexible Consumers

We consider the problem of designing an expected-revenue maximizing mechanism for allocating multiple non-perishable goods of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> varieties to flexible consumers over <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> time steps. In our model, a random number of goods of each variety may become available to the seller at each time and a random number of consumers may enter the market at each time. Each consumer is present in the market for one time step and wants to consume one good of one of its desired varieties. Each consumer is associated with a flexibility level that indicates the varieties of the goods it is equally interested in. A consumer&#x2019;s flexibility level and the utility it gets from consuming a good of its desired varieties are its private information. We characterize the allocation rule for a Bayesian incentive compatible, individually rational and expected revenue maximizing mechanism in terms of the solution to a dynamic program. The corresponding payment function is also specified in terms of the optimal allocation function. We leverage the structure of the consumers&#x2019; flexibility model to simplify the dynamic program. Our simplified dynamic program allows us to provide an explicit allocation procedure and a simple payment rule in terms of the solution of the dynamic program.

Robust energy management in isolated microgrids with hydrogen storage and demand response

Hydrogen is called to be one of the main energy vectors in future energy systems, especially for energy storage, where this carrier presents some interesting features. Its application in microgrids might help to improve economic, environmental and reliability indicators, providing greater storage capacity than other technologies, like batteries. Following this idea, this paper develops a robust energy management methodology for isolated microgrids considering hydrogen storage and demand response initiatives. The new proposal is raised as a nested max-min optimization framework. To reduce the original problem to a tractable single-level one, a master-slave scheme is developed by which binary variables can be fixed and thus the inner problem can be reduced to its equivalent Karush-Kuhn-Tucker conditions. Then, the overall optimization paradigm is solved in an iterative fashion used the Constraint-and-Column Generation Algorithm. The resulting tool is applied to a benchmark isolated microgrid, thus validating it for industry applications. Moreover, different relevant results are analysed in-depth. Thus, the role of hydrogen storage and demand response initiatives is discussed, concluding that flexible demand has a more notable impact on monetary savings than hydrogen storage, reducing the total cost by 6 % with respect to the base case. Some intrinsic issues are also identified. For example, it is observed that flexible consumers are more frequently called when the hydrogen chain is enabled, which may provoke undesirable effects like response fatigue.

Using existing cold stores as thermal energy storage.

The industrial cold stores can act as thermal energy stores that can store the energy as passive thermal energy. The cold stores have intentions to contribute with flexible consumption but need some knowledge about the potential. By cooling the cold stores and the goods further down when the energy is cheaper, there is a potential of an attractive business case, especially if the elspot price can be predicted further into the future. The cold stores can provide flexibility by load shifting to the energy grid by moving their extensive energy use to off-peak hours. To fulfill the potential, it is necessary to measure some data in the cold stores to be able to control them and ensure food safety. A case study was tested and revealed that by cooling further in periods of low-cost electricity could results in 30% savings. With proper elspot price forecasting this percentage could reach up to 40%. Theoretically, by utilizing the full capacity of cold stores in Denmark for thermal energy storage, it is possible to use 2% of the average wind electricity production.