Interactive Energy Management Research Articles

Hybrid Game Trading Mechanism for Virtual Power Plant Based on Main-Side Consortium Blockchains

With the rapid development and technological innovation in the energy market, peer-to-peer (P2P) energy trading, as a decentralised and efficient trading model, has been widely studied and practically applied. However, in P2P energy transactions involving multiple prosumers, there are challenges such as information asymmetry, trust issues, and transaction transparency. To address these challenges, blockchain technology, as a distributed ledger technology, provides solutions. In this paper, we propose a blockchain technology-based prosumer–virtual power plant (VPP) two-tier interactive energy management framework to assist P2P energy transactions between multiple prosumers. In this framework, the virtual power plant acts as a leader and sets differentiated tariffs for different prosumers to equal the distribution of social welfare. The various prosumers act as followers and respond to the leader’s decisions in a cooperative manner. Blockchain’s immutability and transparency enable prosumers to participate in P2P energy trading with greater trust, share idle energy, and share revenues based on contribution. In addition, given the uncertainty of renewable energy, this paper employs a stochastic planning approach with conditional value at risk (CVaR) to describe the expected loss of VPP. Ultimately, as verified by the arithmetic simulation, the blockchain co-governance transaction model effectively supports energy coordination and optimization of complementarities while ensuring the utility of each transaction node. This model promotes the application of renewable energy in local consumption, while facilitating the innovation and sustainable development of the energy market.

Population mobility network and spatial accessibility based on peer-to-peer interactive energy management

With the advancement of science and technology and the development of the world economy, the global demand for energy is increasing day by day. Therefore, it is necessary to reduce the utilization rate of fossil fuels. The key is to promote the rapid development of renewable energy and increase the proportion of new energy consumption. Therefore, accelerating the transformation and upgrading of the energy structure has become the top priority of future energy policies, and energy management has also become a hot topic in contemporary times. The high-speed and free population flow between cities has promoted the flow of production factors within the region, and a population flow network is slowly forming. The spatial accessibility index is used to evaluate the rationality of the spatial layout of urban facilities, and can also be used to test the service efficiency and the fairness of spatial distribution. This paper explores population mobility networks and spatial accessibility based on peer-to-peer interactive energy management. The purpose is to propose a good population flow network and spatial accessibility research method to explore the urban population flow characteristics and spatial characteristics with higher efficiency. This paper first introduces related methods for interactive energy management, population mobility networks, and spatial accessibility. A network analysis model of population flow is proposed, and the evaluation index and method of spatial accessibility are proposed. Finally, two experiments are done in this paper. First, we study the population mobility network in Beijing, Shanghai, Guangzhou, and Chongqing. The result is a correlation between the average distance of the network edges and the node degree value. The larger the degree value, the larger the average distance. The second experiment investigates the spatial accessibility of commercial facilities in Hangzhou. It is concluded that the difference between the maximum and minimum values of Hangzhou's total clustering degree is 86.3, and the standard deviation is 38.98. It proves that the global integration of Hangzhou is unbalanced and the degree of agglomeration is not high.

FEEdBACk: An ICT-Based Platform to Increase Energy Efficiency through Buildings’ Consumer Engagement

Energy efficiency in buildings can be enhanced by several actions: encouraging users to comprehend and then adopt more energy-efficient behaviors; aiding building managers in maximizing energy savings; and using automation to optimize energy consumption, generation, and storage of controllable and flexible devices without compromising comfort levels and indoor air-quality parameters. This paper proposes an integrated Information and communications technology (ICT) based platform addressing all these factors. The gamification platform is embedded in the ICT platform along with an interactive energy management system, which aids interested stakeholders in optimizing “when and at which rate” energy should be buffered and consumed, with several advantages, such as reducing peak load, maximizing local renewable energy consumption, and delivering more efficient use of the resources available in individual buildings or blocks of buildings. This system also interacts with an automation manager and a users’ behavior predictor application. The work was developed in the Horizon 2020 FEEdBACk (Fostering Energy Efficiency and BehAvioral Change through ICT) project.

Three‐level interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

<scp>Three‐level</scp> interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System Considering Quality of Service

In recent years, electric vehicle charging stations (EVCSs) have been deployed very fast, which offer a platform for the energy coordination between electric vehicles (EVs) and the electricity distribution system (DS). This paper presents an energy management method for the operation of EVCSs and DS while considering their interaction. In this method, we first use a quality-of-service constrained decision model for EVCSs, which is formulated based on the network calculus theory. Then, the DS aims to maximize its utility by scheduling charging plans for EVCSs. Afterwards, a game based on the supply function equilibrium (SFE) model is adopted to analyze the energy interaction, in which each EVCS is regarded as the leader, and the DS as the follower. In this way, an optimization problem with equilibrium constraints is established. Moreover, a hybrid optimization algorithm based on differential evolution and interior point method is used to obtain the equilibrium solution of the SFE game. Finally, simulation studies verify the effectiveness of proposed interactive energy management method.

Interactive Energy Management for Enhancing Power Balances in Multi-Microgrids

This paper proposes a bi-layer game-theoretic framework for the interactive energy management (IEM) of multi-microgrids (MMGs) under the presence of uncertainties imposed by both supply and demand sides. The higher-layer of the framework manages the energy trading and the consumption behavior for each microgrid (MG) within a slow timescale, where its operation cost model is designed in terms of economic factors and users’ willingness. The lower-layer runs at higher frequency that relies on a model predictive control approach, and adjusts the operations of MGs to minimize the difference between the planned strategies generated by the higher-layer and the real ones. In doing so, the supply and demand uncertainties as well as the outage events can be handled properly. As the internal prices are adjusted with system net load, the power balance levels in MMG would be enhanced by minimizing operation cost. On this basis, the energy trading among MGs can be achieved directly without an intermediary. We design a decentralized interactive algorithm to implement the bilayer IEM framework by using the Nash equilibrium concept. Furthermore, the emergency situation is covered to enhance the resilience of the system. Numerical simulations on a practical MMG verify the effectiveness of the proposed models.

Real-Time Wireless Energy Management System of Miniature Standalone Photovoltaic Application

Standalone photovoltaic (PV) system is practical and beneficial for the place where the physical power transmission line is not feasible. Nevertheless, for miniature scale standalone PV system, efficient and interactive energy management is extremely necessary to ensure that the battery is capable of supplying energy to the load uninterruptedly without any failure as its condition is being monitored, time by time. In this paper, a real-time energy management system equipped with wireless monitoring features has been developed and tested. All energy information such as battery voltage, generating current, discharging current, etc. are well displayed and extracted directly from the developed system. The high-power switching system is able to be controlled remotely as several MOSFETs attached to the main circuit are feasible to be flagged on or off via Blynk application instantaneously without any time lag. Therefore, the proposed system may become a good model for better small-storage-capacity based real-time energy management system in the future.

Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

An open solution to provide personalized feedback for building energy management

AbstractThe integration of renewable energy sources increases the complexityin mantaining the power grid. In particular, the highly dynamic nature ofgeneration and consumption demands for a better utilization of energy re-sources, which seen the cost of storage infrastructure, can only be achievedthrough demand-response. Accordingly, the availability of energy and po-tential overload situations can be reflected using a price signal. The effec-tiveness of this mechanism arises from the flexibility of device operation,which is nevertheless heavily reliant on the exchange of information be-tween the grid and its consumers. In this paper, we investigate the capabilityof an interactive energy management system to timely inform users on en-ergy usage, in order to promote an optimal use of local resources. In partic-ular, we analyze data being collected in several households in Italy and Aus-tria to gain insights into usage behavior and drive the design of more effec-tive systems. The outcome is the formulation of energy efficiency policiesfor residential buildings, as well as the design of an energy management sys-tem, consisting of hardware measurement units and a management software.The Mjolnir framework, which we release for open use, provides a platform¨where various feedback concepts can be implemented and assessed. This

A simple energy usage toolkit from manufacturing simulation data

A fundamental problem in the management of energy usage is the inability to clearly predict any possible energy saving opportunities. The cost of both under or overestimating potential returns on investment can be prohibitive to a decision maker. In recent years the simulation of energy usage using existing manufacturing simulation tools has increased in popularity among researchers, but it is decision makers who need to see the benefits of this discipline. This paper proposes an interactive manufacturing energy management toolkit which makes use of existing productivity simulation models for the prediction of energy usage. An interactive Microsoft® Excel® based toolkit is developed to control Lanner's WITNESS® discrete-event simulation software using Microsoft® Visual Basic® for Applications. The toolkit has the ability to predict potential areas where energy saving opportunities can be made within a complex manufacturing line, and is accessible from management presentations and proposals. The interactivity of the toolkit provides an environment which facilitates efficient hypothesis testing. The paper includes an industrial case study where the approach was used to quantify theoretical savings from certain energy usage reduction scenarios within a complex automotive engine manufacturing line.

Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources

Recently, large-scale renewable energy resources have been widely integrated into power systems. To optimize large-scale integration of these resources and improve the operation performance of the distribution system, this paper proposes a novel dynamic energy management strategy with the cooperative interaction of an energy system: a multi-grid connected microgrids (MGs)-based active distribution system (ADS). A bi-level multi-objective optimization problem of the strategy is formulated with the active distribution network (ADN) in the upper level and MGs in the lower level. The interaction can be classified into two categories: the one between MGs and the ADN and the other one among MGs. The former is described by bi-level programming; the latter is innovatively explained by an interactive energy game matrix (IEGM) defined in this paper. The concept of the expanded energy storage system is defined and applied to the IEGM for the optimal operation of ADSs. The optimal operation includes improved technical performances in terms of power quality, energy utilization, adaptability and autonomy. The optimization problem is solved with a hybrid algorithm of Rough Set Theory–Hierarchical Genetic Algorithm–NSGA-II. Case studies of an ADS with different MGs and a real system would validate the efficiency of the proposed methodology. Results show that the proposed EM strategy can accurately quantify and guide the energy interaction among MGs and that between MGs and the ADN. Moreover, those technical performances of the ADS are improved.

Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture

In this paper, a comprehensive real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed. A hierarchical bi-level control scheme (BLCS) with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists of sub-components of load demand prediction, renewable generation resource integration, electrical power-load balancing, and responsive load demand. In the primary level, EMSs are operating separately for each microgrid (MG) by considering the problem constraints, power set-points of generation resources, and possible shortage or surplus of power generation in the MGs. In the proposed framework, minimum information exchange is required among MGs and the distribution system operator. It is a highly desirable feature in future distributed EMS. Various parameters such as load demand and renewable power generation are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi $^\prime$ s orthogonal array testing approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS in the secondary level. In order to validate the proposed control structure, a test system is simulated and optimized based on multiperiod imperialist competition algorithm. The obtained results clearly show that the proposed BLCS is effective in achieving optimal dispatch of generation resources in systems with multiple MGs.

Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market

A virtual power plant takes advantage of interactive communication and energy management systems to optimize and coordinate the dispatch of distributed generation, interruptible loads, energy storage systems and battery switch stations, so as to integrate them as an entity to exchange energy with the power market. This paper studies the optimal dispatch strategy of a virtual power plant, based on a unified electricity market combining day-ahead trading with real-time trading. The operation models of interruptible loads, energy storage systems and battery switch stations are specifically described in the paper. The virtual power plant applies an optimal dispatch strategy to earn the maximal expected profit under some fluctuating parameters, including market price, retail price and load demand. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and is solved by the fruit fly algorithm.

Optimal Energy Management for Smart Distribution Grid Based on Virtual Power Plant

Virtual Power Plant (VPP) technology can take advantage of interactive communication and energy management system to optimize and coordinate the control of distributed generation, controllable load, energy storage device, so as to integrate them and participate in the energy exchange of the grid and the quote trading of power market. This paper is devoted to study the energy management system in virtual power plant. Under the day-ahead market ,this project puts forward the maximum profit model, analyzes the operation condition of various elements in virtual power plant, and set restrictions for its operation and energy storage; According to the randomness of the energy resources such as wind and light in actual operation as well as the price in power market, the change of power grid load and electricity demand, and the fluctuations in the retail price of electricity, this paper adopts the proposed model of optimal allocation to coordinate and optimize the configuration of the elements in virtual power plant.

An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty

In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

EMDSS: An optimization-based decision support system for energy systems management under changing climate conditions – An application to the Toronto-Niagara Region, Canada

Management of energy systems is a challenging task, involving a large number of social, economic, environmental, technical, and political factors. This challenge is being complicated with growing concerns of climate change impacts on various natural processes and human activities. To comprehensively deal with such complexities, system analysis approach are desired as it can address various impact factors and facilitate the assessment of policy consequence and climate change response within an interactive energy management systems. The objective of this study is to develop an optimization-based decision support system (EMDSS) and the relevant software package to provide comprehensive analysis of climate change impacts, and energy and environmental policy responses within an energy management system framework. EMDSS is then applied to the Toronto-Niagara Region (TNR), Canada. The results indicated that EMDSS was effective for analyzing and visualizing various scenarios within energy management systems. The obtained information can direct policy makers to initiate strategies for dealing with various issues of climatic change, energy development and environmental management.

Planning of energy system management and GHG-emission control in the Municipality of Beijing—An inexact-dynamic stochastic programming model

Concerns over increasing energy price, exacerbating power shortage and changing climatic conditions are emerging associated with municipal energy management systems. Most of the previous studies could hardly address multiple uncertainties that exist in such systems; this may lead to incomplete, simplified or even false solutions, and could jeopardize the robustness of management decisions. This study is to develop a dynamic inexact stochastic energy systems planning model (DITS-MEM) for managing municipal energy systems and greenhouse-gas (GHG) emissions under uncertainty. Through integrating mixed-integer, interval-parameter and two-stage stochastic programming techniques, DITS-MEM can handle not only the uncertainties expressed as interval values and probabilistic distributions associated with GHG-emission reduction targets, but also the dynamics of capacity-expansion issues. The developed model is then applied to the Beijing Municipality. The results suggest that the DITS-MEM model is applicable in reflecting complexities of multi-uncertainty, dynamic and interactive municipal energy management systems, and capable of addressing two-stage stochastic problem of GHG-emission reduction. The obtained solutions can provide decision bases for formulating GHG-reduction policies and assessing the associated economic implications in purchasing emission credits or bearing economic penalties.

Uncovering energy-efficiency opportunities in data centers

The combination of rapidly increasing energy use of data centers (DCs), which is triggered by dramatic increases in IT (information technology) demands, and increases in energy costs and limited energy supplies has made the energy efficiency of DCs a central concern from both a cost and a sustainability perspective. This paper describes three important technology components that address the energy consumption in DCs. First, we present a mobile measurement technology (MMT) for optimizing the space and energy efficiency of DCs. The technology encompasses the interworking of an advanced metrology technique for rapid data collection at high spatial resolution and measurement-driven modeling techniques, enabling optimal adjustments of a DC environment within a target thermal envelope. Specific example data demonstrating the effectiveness of MMT is shown. Second, the static MMT measurements obtained at high spatial resolution are complemented by and integrated with a real-time sensor network. The requirements and suitable architectures for wired and wireless sensor solutions are discussed. Third, an energy and thermal model analysis for a DC is presented that exploits both the high-spatial-resolution (but static) MMT data and the high-timeresolved (but sparse) sensor data. The combination of these two data types (static and dynamic), in conjunction with innovative modeling techniques, provides the basis for extending the MMT concept toward an interactive energy management solution.