Dynamic Demand Response Research Articles

Stability analysis of distributed smart grid based on machine learning

Decentralized Smart Grid (DSG) is a new technology proposed to power networks with elastic nodes. It can realize dynamic electricity price demand response without large-scale transformation of the infrastructure.In order to analyze the system stability of DSG, six representative machine learning classification models were applied to analyze the stability data of 10,000 samples of the 4-node system. Combined with the requirements of power system security, stability and economic performance, the effect of each classification model on the stability prediction of DSG system was tested.The test results showed that the model with a Gaussian kernel basis function kernel support Vector machine (RBF SVM) was suitable for data analysis, with the accuracy up to 97.10% and the F metric up to 0.977.CART decision tree model is suitable for real-time forecasting of power system. Under the requirement of ensuring real-time forecasting, its accuracy can reach 84.90% and F metric can reach 0.882. Its modeling and prediction calculation requirements are only 0.98% and 1.59% of that of RBF SVM model respectively.

Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating

Applications of electric heating, which can improve carbon emission reduction and renewable energy utilization, have brought new challenges to the safe operation of energy systems around the world. Regenerative electric heating with load aggregators and demand response is an effective means to mitigate the wind curtailment and grid operational risks caused by electric heating. However, there is still a lack of models related to demand response, which results in participants not being able to obtain maximum benefits through dynamic subsidy prices. This study uses the Weber–Fechner law and a clustering algorithm to construct quantitative response characteristics models. The deep Q network was used to build a dynamic subsidy price generation framework for load aggregators. Through simulation analysis based on the evolutionary game model of a project in a rural area in Tianjin, China, the following conclusions were drawn: compared with the benchmark model, regenerative electric heating users can save up to 8.7% of costs, power grid companies can save 56.6% of their investment, and wind power plants can increase wind power consumption by 17.6%. The framework proposed in this study considers user behavior quantification of demand response participants and the differences among users. Therefore, the framework can provide a more reasonable, applicable, and intelligent system for regenerative electric heating.

Analysis of Smart Grid Stability and Security Management Based on Data Mining

In the process of the operation and construction of the smart grid, the development of science and technology and the improvement of social productive forces have put forward higher requirements for the development of the power system. In this process, the scale and form of the power system have also changed to a certain extent, and power security has also become the most concerned issue. In the information society, through the application of the Internet of things and big data technology in power security management and control. It can simplify the operation process of the staff and reduce the occurrence of accidents. Distributed smart grid is a new technology proposed for power networks with elastic nodes, which can realize dynamic electricity price demand response without large-scale transformation of infrastructure. In order to analyze the system stability of DSG, six representative machine learning classification models are applied to analyze the stability data of 10000 samples of 4-node system. Combined with the requirements of power system security, stability and economy, the effect of each classification model on the prediction of DSG system stability is tested.

Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: A bi-level fuzzy approach

This paper proposes a stochastic multi-objective optimization framework to design real-time pricing for transactive energy. The energy transaction among the load-serving entity and customers is formulated as a bi-level optimization in which load-serving entity acts as the leader in the upper-level of optimization aiming to profit and reserve maximization. To make the interaction between opposite objectives, the modified fuzzy method is employed. The load-serving entity takes part in the wholesale market and purchases energy from the wholesale market and sells it to the customers. The load-serving entity itself possesses some dispatchable resources and it can also buy energy from privately-owned renewable resources. On the other hand, demand response providers are buying energy from the load-serving entity on behalf of consumers. The demand response providers are the followers of the proposed model and seek to maximize the profit of their customers. The communication between LSE and customers can be provided by the energy internet. Using the energy internet, the LSE controls and monitors the behavior of costumers to design real-time prices. The proposed model is tested on a standard case study, and the results show that the reservation and energy not supplied have been improved as 12% and 43.8%, respectively.

An Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks

The technology-driven shift in distributed network systems, Internet technology and energy grid digitization have changed the traditional spoke-hub distribution models to the more flexible and giant network-to-networks paradigm known as Energy Cloud. This energy cloud paradigm has the ability to adapt to the large-scale distributed energy resources with dynamic demand–response associated with smart grid and smart homes applications, which allows customers/users to have connectivity of electrical devices with supply grid into one giant energy cloud network. If efficient policies are not adapted in the design and operations of such networks, sensor devices may sense and forward redundant data packets. Hence, valuable energy of the node will be depleted quickly and as a result; the network will be down before accomplishing its intended task. To address the issue of energy-efficiency in energy-cloud at embedded networks, this paper proposes a novel scheme, “Energy Efficient Adaptive Scheduling Scheme (EASS) for Mesh Grid Wireless Sensor Networks”. In EASS, a sensor node configures and schedules its functions/roles according to the contents of sensed data packets and frequency of generated traffic. Like energy cloud, tasks are uniformly distributed on all nodes in mesh-grid in four states – each state of a node is responsible for configuring its components on a specific energy level – which aims to avoid link disconnection and vanish redundant data packets generation. Simulation results show that EASS increases energy-efficiency by 50% due to the four-states model, 62.5% due to alive nodes, 92.60% due to minimized Cluster-Heads overhead and 38.11% due to the reduction in dead nodes ratio.

Stochastic optimization of integrated energy system considering network dynamic characteristics and psychological preference

To improve the flexibility of integrated energy system (IES) and promote the clean energy accommodation, an IES stochastic optimization model considering the network dynamic characteristics and psychological preference is proposed. Firstly, the uncertainty and spatial-temporal correlation between multiple wind farms are dealt by the auto-regressive and moving average (ARMA) time series and Cholesky decomposition, and then typical scenarios are constructed by the scenario method. Secondly, considering the dynamic characteristics of heat and gas networks such as heat network delay, thermal inertia of buildings and gas linepack, the IES model which coordinates the supply side, transmission side and demand side is proposed. Finally, based on the mean-variance Markowitz theory and endowment effect in behavioral psychology, the influence of human psychological preference (such as risk concern and loss aversion) on the IES is reflected. A 52-node electricity-gas-heat IES is used to verify the proposed model. The simulation results reveal that the network dynamic characteristics and demand response (DR) can effectively improve the IES flexibility and wind power accommodation. Meanwhile, the psychological preference and wind power spatial-temporal correlation also have significant influence on the IES optimization.

Examination of optimum benefits of customer and LSE by incentive and dynamic price-based demand response

ABSTRACT The balancing approach of electricity generation and consumption is an essential part of a reliable power system. The mismatch between supply and demand may also cause fluctuation in the power system. This study proposes an incentive and penalty-based demand response (I&P-DR) program for improving the profitability of both LSE and customers. First, we use a parameter that weighs the financial benefits of LSE and customers and provides considerable economic benefits to both sides. Second, an incentive and penalty (I&P) price scheme have been employed to recompense and penalize customers and reduce the electricity demand at peak hours. Finally, the study analyzes the importance of (I&P-DR) and its impact on customers’ sensitivity during peak intervals. Simulation results showed that the flexibility to consumption can be brought through the application of the proposed (I&P-DR) program and also provide the financial benefits to both, customers and LSEs.

Designing a large-scale public transport network using agent-based microsimulation

Abstract An agent-based simulation framework is extended to design efficient large-scale public transport networks. It goes beyond existing approaches by incorporating a dynamic demand response towards both changes in the network and exogenous factors. The framework is based on an agent-based (MATSim) simulation and tested for the city of Zurich. In contrast to most existing public transport network optimization approaches, this framework is part of a city-level, multi-modal transport simulation tool. Compared to the existing public transport system, it proposes a sparser network with smaller vehicles and substantially higher frequencies. By doing so, the approach predicts a higher transit ridership at a lower level of subsidies. Moreover, it reliably identifies corridors for potential capacity upgrades. The method may help transport planners to assess their existing public transport networks and to plan public transport infrastructure for the era of automated vehicles.

RETRACTED ARTICLE: A Decentralized Dynamic Marketing-Based Demand Response Using Electric Vehicles in Smart Grid

Due to different energy storage capabilities of electric vehicles and their unpredicted charging/discharging patterns, the load uncertainty has significantly increased on the utility grid. This load uncertainty has also enabled the elasticity in the power production through intermittent energy resources. This emerging electricity generation elasticity could be used to optimize aggregator load profile by altering real-time demand of a power network, particularly in the premises of a massively EVs surrounded grid. In this paper, we have proposed a demand response algorithm to optimize vehicle to grid (V2G) aggregation, by enabling EVs scheduled charging/discharging, in quest to minimize the energy cost for the retailer, as well as satisfying the electricity market, i.e., day-ahead and instantaneous real-time markets. We have employed a decentralized approach in our proposed scheme, which is accessible, allows fast convergence, and assures consumers’ privacy. The simulation results show that our proposed technique significantly reduces the energy cost, explicitly for a retailer who tends to optimize the power network by penetrating an efficient algorithm. In addition, our algorithm shows more relevance when the grid leniently manages several intermittent energy generation resources. Practically the real-time load variations are uncertain, and are difficult to control, which increases the power and cost variations, and some time ends up as complete system failure. By using this algorithm the energy retailers can offer reliable service and suitable tariffs to consumers, to prove relevancy by restricting such constraints.

Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

Probabilistic Peak Demand Matching by Battery Energy Storage Alongside Dynamic Thermal Ratings and Demand Response for Enhanced Network Reliability

Battery energy storage systems (BESS), demand response (DR) and the dynamic thermal rating (DTR) system have increasingly played important roles in power grids worldwide. In addition to storing energy, BESS can supply peak demands, thereby reducing the frequency of load interruptions and deferring new asset investments. However, study on the precise BESS sizing (i.e. energy and power ratings) to supply peak demands to improve the security of supply of transmission networks is still lacking. The combined efficacy of BESS, DR and DTR have also never been studied, because their simultaneous deployment has never been considered. The first contribution of this paper is proposing a probabilistic evaluation method to evaluate various combinations of BESS power ratings and energy capacities and determines their impacts on the reliability of transmission networks, in which peak demands are supported by charges stored in BESSs to address the security of supply problem. The second contribution extends the proposed method to examine the effects of deploying BESS alongside DR and DTR. Our results show that the security of power supply improves along with BESS sizing by as much as 37.2%, and that its reliability becomes more significant as its capability grows, with bigger BESS having more detrimental effects towards EENS as it becomes unavailable than smaller BESS does. DTR and DR reduce the requirements of BESS sizing without adversely affecting network reliability.

RETRACTED: Modeling and prioritizing dynamic demand response programs in the electricity markets

Abstract Integration of demand and supply-side resources has been performed by electric utilities to benefit their potential attractiveness in both economic and operation levels. The Demand Response Resources (DRRs) can be considered as one of the most important demand side options which have been developed as an aftermath of DRPs implementation. In this paper, based on benefit function concept of customers and flexible demand elasticity, a dynamic economic model of DRPs as a combination of EDRP and TOU programs is developed. Different alternatives of DRPs are developed in which the independent system operator can choose the optimal DRP which reflects its perspectives. To do so, the Multi Attribute Decision Making (MADM) is employed as an effective method. In addition, entropy methods and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are utilized to prioritize various developed DRPs. To cover all operating conditions, the numerical studies are provided on a standard IEEE ten-unit test system taking from both “load profile characteristics” and “economical” points of view into account and considering different indices as peak to valley distance, peak reduction, etc. Based on obtained results, the developed model can be utilized to improve satisfaction of customers and the load profile characteristics.

Non-cooperative differential game based energy consumption control for dynamic demand response in smart grid

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users' energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning

A feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.

A Simplified Regression Model for Dynamic Demand Response Process of Building Loads

This paper presents a simplified regression model for building dynamic DR processes based on the mechanism model to facilitate the timely analysis, prediction and optimization of aggregation DR characteristics of a large-scale diversified building load. First, numerous simulations based on the mechanism model are performed to obtain sample data of building DR under different external conditions. Second, the key factors influencing the dynamic demand response process of a building—namely, the climbing period, stable response period and recovery period—are analyzed. Finally, this paper analyzes the simulation error and calculation efficiency of the proposed simplified regression model. Furthermore, the calculation speed is sufficiently fast to meet the requirements of real-time scheduling.

A data-driven methodology for dynamic pricing and demand response in electric power networks

The practice of disclosing price of electricity before consumption (dynamic pricing) is essential to promote aggregator-based demand response in smart and connected communities. However, both practitioners and researchers have expressed fear that wild fluctuations in demand response resulting from dynamic pricing may adversely affect the stability of both the network and the market. This paper presents a comprehensive methodology guided by a data-driven learning model to develop stable and coordinated strategies for both dynamic pricing as well as demand response. The methodology is designed to learn offline without interfering with network operations. Application of the methodology is demonstrated using simulation results from a sample 5-bus PJM network. Results show that it is possible to arrive at stable dynamic pricing and demand response strategies that can reduce cost to the consumers as well as improve network load balance.

Retail market equilibrium and interactions among reconfigurable networked microgrids

In this paper, a retail market equilibrium among a set of competing players, i.e., retailers and Microgrids (MGs), considering Dynamic Line Rating (DLR) constraint and demand response (DR) actions is modeled. In this regard, a multi-follower-type bi-level optimization scheduling for decision making of Distribution System Operator (DSO) and reconfigurable networked MGs with inherently conflicting objectives is presented. In this model, the upper-level player of the Main Problem (MP) minimizes the total cost from DSO’s perspective, while in the lower-level of the MP, networked MGs compete with retailers in the retail electricity market as an interior Sub Problem (SP). In the upper-level of the SP the profit of each MG is maximized, where in the lower-level of the SP the social welfare of the retail market clearing process is maximized. Since the resulting bi-level problem is nonlinear and non-convex, it is transformed into a single-level Mixed-Integer Second-Order Cone Programming (MISOCP) problem using Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques. The effectiveness of the proposed model is investigated on a real-test system in both grid-connected and islanded modes under different scenarios.

Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

Operating mechanism for profit improvement of a smart microgrid based on dynamic demand response

A microgrid helps to reduce the consumption of fossil fuel by utilising distributed renewable energies (DREs). However, the stochastic nature of renewable energy makes it a big challenge for the microgrid operators to maximise the consumption of DREs while ensuring the balance between supply and demand. In order to improve the consumption of DREs and the profit of a microgrid efficiently, an operating mechanism consisting of two stages is proposed to dispatch DREs, responsive loads and plug-in hybrid electric vehicles (PHEVs) in a smart microgrid. In the first stage, we formulate the optimal unit commitment and the day-ahead time of use, which the operator concerns as a decentralised model. To address the model, the particle swarm optimisation algorithm and the interior point method are used, respectively, to minimise the operating cost and the electricity import. In the second stage, a dynamic incentive signal-based demand response for PHEVs is proposed to maximise the electricity export in the intra-day. Simulation studies are conducted on a smart microgrid, and the results have demonstrated that the proposed operating mechanism improves the consumption of DREs while satisfying the demand, meanwhile improves the average daily profit of the smart microgrid.

Optimization modeling for dynamic price based demand response in microgrids

Eco-friendly technologies regarding electricity production are urgent need of sustainable energy development. The renewable energy resources (RERs) have brought green revolution in mitigation of greenhouse gaseous emission resulted from traditional energy resources (TERs). Moreover, the effective utilization of these resources is influenced by pricing schemes which have limitations. Therefore, this paper aims at optimization modeling for dynamic price-based demand response (DR) which includes flexible and inflexible loads along with the effective utilization of RERs i.e. photovoltaics (PVs) and wind turbines (WTs) in a microgrid (MG). The optimization problem regarding profit maximization for loads (flexible and inflexible) is solved via particle swarm optimization (PSO). Two cases are used to evaluate the performance of proposed dynamic pricing scheme. The simulation results have shown that proposed scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases. In addition, the dynamic pricing scheme is exemplified as plug and play devices because of its easy implementation in present market structure without any modification.