Demand Side User Research Articles

Low-carbon operation strategy of virtual power plant considering progressive demand response

Adapting virtual power plants (VPPs) to the development of new power systems has gradually become a trend. Meanwhile, the economical and effective use of demand-side resources in VPPs has become a research hotspot in the field of new energy. This paper proposes a low-carbon operation strategy with progressive demand response (DR). A DR model id established by evaluating the scheduling potential of low-carbon and flexible loads and the DR transfer of different users motivated by incentive mechanisms. Because the changing carbon price in carbon trading also affects the response transfer amount, it is beneficial to reduce the operating cost of the energy system by using consumers’ surplus value according to consumer psychology to guide energy users to participate in DR. Finally, wind, light, user load data and VPP internal unit output are optimized in rolling mode over multiple time scales, and the optimal adjustment results of the previous day are taken as the basis. At the same time, the output deviation penalty function is added to further reduce the system cost. The nonlinear model of DR cost is transformed into a mixed integer linear model, which is modeled on MATLAB software and solved by a CPLEX optimization solver. The case analysis shows that the progressive DR strategy may increase the total operating cost of the system, but reduce the cost of the carbon emission. The proposed model and method can not only improve the feasibility and effectiveness of market supervision strategy, but also provides a useful reference for operators and demand-side users in energy systems to choose strategies.

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

Abstract With the widespread adoption of distributed new energy and residential energy storage (RES), demand-side users in active distribution networks (ADNs) are transitioning from traditional consumers to prosumers. The flexible resources of demand-side prosumers require an appropriate trading mechanism to achieve optimal resource allocation. A demand-side energy sharing mechanism based on residential energy storage is proposed. Firstly, a trading decision model for prosumer units is established by integrating the charge-discharge utility of RES devices with production-consumption characteristics. Then, a decentralized social welfare maximization energy sharing problem for ADNs is constructed using the Alternating Minimization Algorithm (AMA). Furthermore, a 10-node active distribution network is analyzed as an example. The proposed mechanism can maximize social welfare and increase the adaptation level of new energy from 64.58% to 66.83%.

Optimal bidding strategy for multi-energy virtual power plant participating in coupled energy, frequency regulation and carbon trading markets

Multi-energy virtual power plant (MEVPP) with diversified flexible resources can participate in energy market (EM), frequency regulation market (FRM) and carbon trading market (CM) to obtain extra benefits. However, the complicated market environment provokes grant challenges in the bidding strategy of MEVPP that coordinates heterogenous supply-side devices and demand-side users. To the end, this study proposes a multi-objective bidding strategy for MEVPP comprising various energy flows and multi-energy demand response (MDR) in EM, FRM and CM. Results are delivered that: 1) Compared to the only profit-oriented optimization, the profit of comprise solution under the multi-objective declines by 46% but the satisfaction level improves 8%. Relative to the single satisfaction-oriented optimization, a 5.2% satisfaction loss allows for an approximately 103% growth in profit, realizing a win-win result for MEVPP and users. 2) There were negative profits in the pure EM (Case 1) as well as EM and CM (Case3). Compared with positive profit in EM and FRM (Case 2), the profit in coupled EM, FRM and CM (Case 4) increases by 48.9%. Relative to the CO2 emission in EM and CM, the emission under the proposed strategy mitigates by 9.36%. 3) A carbon base price at 200 CNY/t CO2 and an emission interval length of 5–10 t are recommended to maintain the low-carbon economic operation for MEVPP.

Application of game theory in modern electrical power system (a review)

In recent decades, because of the speedy progress of the smart grid and the deepening of the reforms of the energy system, demand-side users can contribute to the collaboration of the energy network, with the main right to public energy procurement and the main right to sell energy. The demand side of the smart grid and the open energy market provides users with more adoptions, and game theory is expected to become an important tool for optimizing multi-stakeholder decision-making and solving many problems in this area. In this regard, this review article first reviews the recent development of game theory application in modern power systems, in addition to discussing in detail the typical gaming behavior of the current energy demand side. Second, it focuses on the application of game theory mainly in three aspects: distributed energy users, high-energy energy users, and medium- and low-energy users. Game theory is used in optimizing the distributed energy coordination, optimizing the energy purchasing strategy, responding to the needs of commercial and residential users, and ensuring network communication. Later, a comprehensive analysis of recent trends in game theory application in power systems is carried out in detail.

The Importance of Energy Prosumers for Affordable and Clean Energy Development: A Review of the Literature from the Viewpoints of Management and Policy

The release of greenhouse gases has led to increases in global temperatures and triggered an unprecedented array of environmental disasters. The aim of net-zero emissions is to increase the share of the world’s energy supplied by renewable sources as well as to influence consumer behavior to improve the balance between energy supply and demand. Appropriate energy policies can encourage consumers to take a proactive role in the transition to more sustainable forms of energy. Given the circumstances, an increasing number of demand-side users now function as prosumers who produce, store, consume, utilize, and manage energy. To understand the state of the energy prosumers’ business model in this changing sector, a review of the literature related to energy prosumers is made, with a focus on energy efficiency, net-zero emissions, Sustainable Development Goal 7 (SDG 7), energy management efficiency, and Energy prosumer systems. The purposes of this review are (1) to explore the shared models of the energy prosumers, (2) to gain insight into the energy prosumer in different areas, and (3) to identify any gaps in the energy management efficiency research. Finally, we examine the major difference between energy prosumers and their respective relationships to energy sources. The results reveal that for energy prosumer systems, there is still some room regarding how they will financially, equitably, and efficiently adapt to the impact of the new increased renewable energy prosumer business model.

Facilitating cooperation among users in water conflicts with a tripartite evolutionary game model: A case study in the Hanjiang Basin, China

Water conflict is evolving into one of the most vital social and environmental issues as the global water crisis intensifies in recent years. Previous studies mostly focus on the game between water users but overlook the fact that water resource institutions (WRIs), which are increasingly involved in water conflicts, are also essential players. In this study, a tripartite evolutionary game model that considers WRIs, demand-side users, and supply-side users is developed. Seven potential evolutionary stable strategies and nine evolutionary scenarios are deduced. The theoretical findings are validated by simulating the water conflict of two provinces (i.e., Hubei and Henan) in the Hanjiang basin, China. The results indicate that non-cooperation among water users and ineffective intervention by WRIs are the long-term outcomes of this water conflict case. To foster cooperation, the game structure needs to be altered by affecting the external variables that determine the net benefits of excess water intake and water rights transfer. Owing to the insufficient long-term incentives for WRIs, cooperation among water users cannot rely on the intervention strategy. Nonetheless, even if external conditions are immature, the intervention strategy can act as auxiliary means to foster cooperation in advance but at the cost of some of the WRIs’ interests. The outcomes of the present study can provide managers of water resources with contributing information for cooperation promoting among water users.

Assessment Method for Demand Side User Response Potential based on Game Theory and Information Value

An accurate assessment of user demand response potential is helpful for power companies or load aggregators to rationally call demand-side resources, improve the implementation effect of demand response, and reduce the peak-valley load difference of power systems. This paper proposes a new approach based on game theory and information value to evaluate the response potential of demand-side users. Firstly, the evaluation index system of user response potential is established based on user history behaviors, user basic information, aggregator’s incentive to users, and user’s information value. Secondly, based on game theory, the total of subjective and objective weights is integrated, and the game solution of evaluation index weight is obtained. Finally, the user potential assessment results are quantified based on the fuzzy optimization set pair analysis theory to achieve an accurate online assessment of the demand-side user response potential.

Demand Response for Optimal Power Usage Scheduling Considering Time and Power Flexibility of Load in Smart Grid

Demand response (DR) shaves peak energy consumption and drives energy conservation to ensure reliable operation of power grid.With the emergence of the smart power grid (SPG), DR has become increasingly popular and highly contributes to energy optimization. On this note, in this work, DR is adopted for scheduling home appliances to reduce utility bill payment, peak to average demand ratio (PADR), and discomfort. First, home appliances are classified into two categories according to time and power flexibility: time-flexible and power-flexible. Secondly, the demand-side users power usage scheduling problem is modelled as per the user priority and modes of operation considering demand and supply. Finally, the energy consumption scheduler (ECS) is developed to adjust the time and power of both types of appliances under different operation modes to acquire desired tradeoff between utility bills payment and discomfort, and PADR and discomfort. Simulation results depict that employing the proposed ECS benefits demand-side users by minimizing their utility bills payment, PADR, and achieving the desired tradeoff between utility bill payment and discomfort, and PADR and discomfort. Results illustrate that developed reduced utility bill payment and alleviated PADR without compromising comfort by 28% and 21%, respectively, compared to without scheduling case.

Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets

More energy flexibility is required to alleviate the stress of power systems caused by intermittent and weather-dependent solar and wind power. The utilization of demand-side flexibility (e.g., flexibility management strategies and distributed storage technologies) can be considered a win-win approach for demand-side users and power grids. However, the economic benefits of distributed energy storage systems in buildings are usually underestimated without considering the full-scale flexibility utilization, which may impede storage investments. Meanwhile, the disposal problem of retired EV batteries is becoming more serious. Repurposing these batteries for stationary applications in buildings seems cost-effective and eco-friendly. Therefore, this study first proposes novel optimal dispatch strategies for different storage systems in buildings to maximize their benefits from providing multiple grid flexibility services simultaneously, and then conducts a comparative life-cycle economic analysis on thermal energy storage, new and second-life batteries. The optimal configuration of hybrid storage systems is also analyzed to facilitate the decision-making of building owners/operators. Test results show that thermal energy storage and electrical energy storage can increase the economic benefits by 13% and 2.6 times, respectively. Battery storage may no longer be an expensive option for building-scale investment due to downward trends in capacity costs and environmental impacts.

Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services

Well-managed demand-side flexibility can effectively alleviate the stress of power systems caused by the increasing renewable energy penetration. The development of smart grid technologies provides win-win opportunities for system operators and demand-side users. Buildings have great flexibility potential through smart control of flexible loads, passive thermal mass storage and distributed active storage. However, a few fundamental problems on flexibility quantification, optimal coordination and profits sharing are faced when aggregating buildings to reach sufficient size for biding multiple flexibility services in electricity markets. Therefore, this paper proposes a multi-level optimal dispatch strategy and profit-sharing mechanism for non-residential building clusters to maximize flexibility profits and reduce the complexity of the optimization. At building level, a model-based quantification method is developed to evaluate flexibility potential of multiple resources. At cluster level, the coordinated bidding strategy of the aggregator is proposed, and flexibility profits are distributed based on the cooperative game theory at the post-market stage. Test results show that the proposed dispatch strategy can reduce up to 20.4% of daily electricity cost at cluster level and ensure profit increase of individual buildings with different flexibility characteristics. The active participation of building clusters in electricity markets could reduce the use of costly backup generators.

Two-stage blockchain-based transaction mechanism of demand response quota

The current price guided demand response mechanism is difficult to accurately achieve the expected load reduction goal for the grid company, while the direct control demand response mechanism cannot meet the requirements of users for autonomous power consumption. Therefore, it is necessary to formulate an incentive compatible demand response mechanism to take the needs of demand-side users and the grid company into account. Based on the current bidding demand response program, a two-stage transaction mechanism of demand response quota based on blockchain is proposed in this work. Firstly, a two-stage transaction mechanism for demand response quota considering day-ahead bidding transaction and intra-day double auction transaction is designed. Then, the smart contract is introduced to realize the proposed blockchain-based two-stage transaction mechanism of demand response quota, and all the smart contract functions involved in each business link are customized. Finally, the effectiveness of the proposed transaction mechanism of demand response quota is illustrated by the simulation results on the platform of Remix IDE.

CSR investment for a two-sided platform: Network externality and risk aversion

The business of two-sided platforms, such as those used for online ride-sharing, has expanded rapidly thanks to opportunities to efficiently match supply- and demand-side users. However, the development of these platforms faces the challenge of complying with corporate social responsibility (CSR) regulations, particularly on the supply side of two-sided platforms. How to improve CSR investment, in order to reduce CSR violations while ensuring profitability, is now an important question. In this paper, we develop a game-theoretic model for a two-sided platform and analyze the impacts of network externalities (including customer network externality and provider network externality) and risk aversion on CSR investment decision-making. The analytical results show that, contrary to our intuition, greater network externalities do not necessarily stimulate the platform to invest in CSR. When the strength of customer network externality is high, investment in CSR may have a “dilution effect”, i.e., CSR investment may reduce the platform's number of users and profit. Conversely, risk aversion always has an “incentive effect” on CSR investment, thereby increasing the platform's willingness to invest in CSR. Second, both provider and customer network externalities will increase the platform's prices. Interestingly, when the strength of customer network externality is high, the platform attracts more customers, even at high prices. Finally, our analysis indicates that risk aversion may reduce customer surplus when the strength of customer network externality is high. This result contradicts the initial expectation that risk aversion inspires platforms to invest in CSR, thereby improving customer utility and customer surplus.

Strategy optimization for virtual power plant complied with power to gas operation model

Virtual power plant (VPP) is an integrated class of integrated power plant composed of energy management system and small and micro distributed energy resources controlled by it. Its distributed energy sources can be distributed generator sets, distributed energy storage equipment or demand response resources distributed among many demand-side users. Its concept emphasizes more on the function and effect presented to the outside world, updating the operation concept and produce social and economic benefits, in line with the needs and direction of China's power development. Given the uncertainty in renewable energy output and demand response, the VPP market of gas networks, Power to Gas (P2G) equipment, flexible loads and other resources are introduced. Furthermore, VPP's optimal scheduling strategy includes considering power and gas scheduling for both power and gas flows, and studies the optimization model considering the dual energy market to maximize the profits of VPP. Through example studies, the economics and scheduling strategies of the proposed VPP are analyzed. Simulation results show that the VPP proposed in this paper can effectively reduce the proportion of renewable energy emissions reduction by selling power or P2G equipment. Moreover, it is conducive to maintaining the pressure stability and safe operation of the natural gas power grid. VPP can reduce the cost of unbalanced punishment and reduce the impact of uncertainty.

Optimal scheduling of Park level integrated energy system considering electrothermal coupling

Considering the inherent characteristics of the park heating load, such as transmission delay, fuzzy heating comfort, etc., it can be used as a flexible load to participate in the optimal scheduling. Aiming at the minimum operation cost of the integrated energy system in the park, a collaborative optimal scheduling model of the park's integrated energy system with the participation of comprehensive demand response of electric heating load is constructed. The simulation results show that, compared with the optimization results of traditional power demand response, the application of integrated demand response of electric heating load improves the flexibility of production of cogeneration units in the park, reduces the total energy consumption cost of demand side users and the operation cost of the system on the premise of ensuring the balance of supply and demand of the system, improves the energy utilization efficiency, and realizes the environmental protection and economy of the system function.

A Deep Learning-Based Strategy to the Energy Management-Advice for Time-of-Use Rate of Household Electricity Consumption

With the high industrialization at a rapid pace, the demand for energy increases exponentially, but it is difficult to meet the balance of demand and supply. Therefore, how to effectively meet the balance of demand and supply intelligently has become a popular issue in this century. In recent years, Taiwan Power Company (Taipower), the biggest electric company in Taiwan, is committed to the construction of Advanced Metering Infrastructure (AMI), which provides communication channels and enables demand-side users to participate in load dispatch. On the other hand, the construction of AMI is expected to generate a tremendous number of valuable data on electricity consumption, but it is not easy to convert these data into effective information by the conventional quantitative methods. In as much as the rapid progression of AI technology in the industrial field, the application of AI technology in the technology management has become an increasing issue as an interdisciplinary study. To address this task, this work applies the recurrent neural network based on deep learning to predict low-voltage usage shortly by the electricity information of low-voltage user and meteorological data. After many vicissitudes, the electricity consumption per hour can be predicted and a sound energy arrangement can be therefore planned. Through introducing the proposed model, Taipower Company will have an effective capability that schedules power, reduces unnecessary backup power, and provides time-consuming electricity prices for industrial enterprises accurately among high usage of Taiwan industries.

An optimal dispatch model based on load aggregator associated with energy hub

With the open of demand side, the demand response will increase the flexibility of power system. As an intermediate between electricity market sellers and demand side users, load aggregator (LA) has more choices in the load shedding transactions with a large amount of demand side resources. This paper considers the impact of energy hub (including electricity energy and heat energy) to the trade strategy of load aggregator and proposes the trading framework of load aggregator based on energy hub (EH), then a load aggregator trading model based on transaction cost is established. Through the linear optimization with trading model, the transaction costs of load aggregator are compared whether the energy hub is used or not, and the influence of efficiency of different modules in energy hub on their supply proportion is analysed, and finally the trading strategy of load aggregator is studied when the demand side resources take part in load shedding.

Optimal configuration of optical storage microgrid under demand-side response based on cooperative game

In the power market environment, considering the influence of the demand-side response and energy storage system on the microgrid, the joint optimization and configuration of the system through a cooperative game approach is proposed, and a time-of-use price is proposed when the demand-side user transfers the load appropriately. The microgrid operation mode is used to maximize the revenue and optimal reliability of the microgrid. Firstly, the users of the load transfer, the users and the objective functions and models of the energy storage system under the time-shared price were established; secondly, the three parties were jointly optimized by cooperative game, and the iterative algorithm was used to find the three parties to optimize the Nash equilibrium (optimal configuration scheme). The system achieves optimal returns and optimal reliability. The model and algorithm were applied to a practical photovoltaic microgrid system, and the effectiveness of the model and algorithm was verified.

A Precision Analysis Method of Electrical Characteristics for Demand Response

In order to accurately grasp the users’ electricity consumption characteristics during the implementation process of the demand response (DR), a clustering algorithm was used to realize delicate analysis of demand-side users. Firstly, the typical load curve of a single user was extracted by means of averaging. Then, an improved adaptive bilayer spectral clustering algorithm was adopted to cluster typical load curves. The simulation results show that the method can accurately analyze the demand-side power load, the inner and outer spectrum clusters can meet the requirements of different demand response projects, can guide the development of the time-of-use price, and can calculate the interrupted response capacity. At the same time, it can provide a theoretical basis for consumption of renewable energy with DR.

Transitioning to a 100% renewable energy system in Denmark by 2050: assessing the impact from expanding the building stock at the same time

Residential and service (office) buildings consume a large proportion of primary energy in Europe in the form of electricity and all other energy carriers. In response to this, the concept of near Zero Energy Buildings (nZEB) has been developed. These buildings have very low energy demands and integrate renewable energy to supply residual demand. nZEBs aim to increase energy efficiency from a demand-side user perspective. When looking at the entire energy system, there are also energy efficiency gains to be achieved on the supply-side. For example, from a district heating system. If an energy system becomes more efficient on the supply-side, then the question is how much energy needs to be saved on the demand-side, for instance by low energy buildings such as nZEBs. The purpose of this paper is to analyse and understand the implications from building new low energy buildings, i.e. nZEBs, within an energy system that is (a) transitioning to 100% renewable energy and (b) has substantially improved supply-side energy efficiency. A case study from Denmark is used to understand the outcome for the energy system when these new buildings are built in this context. The methodology and results of this study could be replicated for other European countries as well. The analysis looks at the total energy system heat savings, costs and biomass consumption. The paper shows that these new low energy buildings with very low heat demand do not deliver the expected benefits for the 100% renewable energy system transition in Denmark. This is due to the increased efficiency and flexibility of the energy supply system in the future. However, deep renovations of existing buildings are necessary. Furthermore, this paper demonstrates based on the Danish case, that as European countries decarbonise their energy systems over the next decades, they will need to carry out detailed energy system analysis to determine the extent to which heat demand should be reduced in buildings within the context of the transitioning energy system.

Distributed demand-side energy management scheme in residential smart grids: An ordinal state-based potential game approach

This paper proposes a distributed demand-side energy management scheme in residential smart grids based on ordinal state-based potential game (SPG) with various kinds of household electrical appliances. Involving the total electricity costs, the supply capacity limits incurred by the distribution infrastructures and the required energy demands for individual appliances, the optimal energy management (OEM) of demand-side users, i.e., homes, turns out to be a complicated optimization problem associated with a coupled objective function subject to spatially and temporally coupled constraints. Such a problem is difficult to solve in a distributed fashion. In this paper, we formulate it as an ordinal SPG, devising a distributed algorithm to achieve the optimum of the original centralized OEM with no need of any central coordinator during the process of execution. Our scheme does not require any private information of individual users to be shared, while both the optimality and convergence are obtained. We also show the scheme is robust to unreliable communications. And the proposed scheme is illustrated and verified by simulations.