The Experts below are selected from a list of 106743 Experts worldwide ranked by ideXlab platform
Yan Gao - One of the best experts on this subject based on the ideXlab platform.
-
optimal dispatching strategy and real time Pricing for multi regional integrated energy systems based on demand response
Renewable Energy, 2021Co-Authors: Guanxiu Yuan, Yan GaoAbstract:Abstract With the penetration of multiple distributed energy sources, demand side management (DSM) of the regional integrated energy system (RIES) becomes more complicated in the energy market. Real-Time Pricing (RTP) is an effective method for DSM, which can flexibly guide the supply and demand sides to adjust their behavior to participate in demand response (DR). In this paper, a hierarchical energy system is studied including multiple RIESs with multiple energy dispatch and supplement. To maximize the social welfare, a bilevel programming model is developed, in which the upper level aims at maximizing the profits of the supplier, and the lower level aims at maximizing the RIESs' welfare. Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. Furthermore, the RTP strategy is obtained, and the optimal energy scheme of RIES is given in the solution. Compared simulations in different scenarios, the total social welfare is increased by about 14.12%, the peak-to-valley difference of power load and carbon emissions are reduced by 16.99% and 5.7% respectively after DR. The results show that the proposed bilevel model under the RTP is conducive to social economy and environment.
-
real time Pricing for smart grid with multi energy microgrids and uncertain loads a bilevel programming method
International Journal of Electrical Power & Energy Systems, 2020Co-Authors: Guanxiu Yuan, Yan Gao, Ripeng HuangAbstract:Abstract As distributed energy (DE) and storage devices being integrated into microgrids (MGs), demand side management (DSM) is getting more and more complicated. The Real-Time Pricing (RTP) mechanism based on demand response (DR) is an ideal method for DSM, which can achieve supply–demand balance and maximize social welfare in the future. This paper proposes a hierarchical market framework to address RTP between the power supplier and multi-microgrids (MMGs). Firstly, an expectation bilevel model is proposed to adjust the energy scheduling of MMGs, including uncertain loads, multi-energy-supply and storage devices,etc. In the proposed bilevel model, the upper level aims to maximize the profit of the power supplier, while the lower level is formulated to maximize the expectation of total welfares for MMGs. Then, the lower level is transformed into a deterministic optimization problem by mathematical techniques. To solve the model, a hybrid algorithm-called distributed PSO-BBA, is put forward by combining the particle swarm optimization (PSO) and the branch and bound algorithm (BBA). In this algorithm, the PSO and BBA are employed to address the subproblems of upper and lower levels, respectively. Finally, simulations on several situations show that the proposed distributed RTP method is applicable and effective under uncertainties, and can reduce the computational complexity as well. The results show that the hierarchical energy dispatch framework is not only more reasonable but also can increase the profits of power suppliers and the welfare of MGs effectively.
-
a rolling penalty function algorithm of real time Pricing for smart microgrids based on bilevel programming
Engineering Optimization, 2020Co-Authors: Li Tao, Ying Liu, Yan Gao, Hongbo ZhuAbstract:A Real-Time Pricing scheme is formulated based on bilevel programming to tackle the uncertainties for smart microgrids equipped with renewable energy sources, dispatchable resources and storage dev...
-
Distributed Real-Time Pricing Method Incorporating Load Uncertainty Based on Nonsmooth Equations for Smart Grid
Hindawi Limited, 2019Co-Authors: Hongjie Wang, Yan GaoAbstract:The Real-Time Pricing mechanism of smart grid based on demand response is an effective means to adjust the balance between energy supply and demand, whose implementation will impact the user's electricity consumption behaviour, the operation, and management in the future power systems. In this paper, we propose a complementarity algorithm to solve the Real-Time Pricing of smart grid. The Karush–Kuhn–Tucker condition is considered in the social welfare maximisation model incorporating load uncertainty to transforming the model into a system of nonsmooth equations with Lagrangian multipliers, i.e., the shadow prices. The shadow price is used to determine the basic price of electricity. The system of nonsmooth equations is a complementarity problem, which enables us to study the existence and uniqueness of the equilibrium price and to design an online distributed algorithm to achieve the equilibrium between energy supply and demand. The proposed method is implemented in a simulation system composed of an energy provider and 100 users. Simulations results show that the proposed algorithm can motivate the users’ enthusiasm to participate in the demand side management and shift the peak loading. Furthermore, the proposed algorithm can improve the supply shortage. When compared with an online distributed algorithm based on the dual optimisation method, the proposed algorithm has a significantly lower running time and more accurate Lagrangian multipliers
-
multi time slots real time Pricing strategy with power fluctuation caused by operating continuity of smart home appliances
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Hongbo Zhu, Yan Gao, Yong Hou, Li TaoAbstract:Abstract Demand side management aims to match power demand to supply through cutting the peak and filling the valley, is one of the most important factors in smart grid. The Real-Time Pricing (RTP) mechanism is an ideal method to adjust power balance between supply and demand. Its implementation has a profound impact on users’ behavior, and on operation and management of the power grid. In this research, we propose an expectation social welfare maximization model, considering the classification of the smart home appliances (SHA) and the correlation of power consumption of multi-time slots. Users can arrange their appliances more profitable and more closely to reality with the advantage of multi-time slots RTP strategy. The constraint in the model reflects the fluctuation (uncertainty) of power consumption caused by operating continuity of the SHA. By introducing probabilistic constraints, the uncertainty optimization model is transformed into a convex optimization problem. The existence and uniqueness of the optimal solution are shown, and its properties are further analyzed. Considering the convex optimization problem is separable in dual domain, this study proposes a decentralized online RTP algorithm to determine each user’s demand and energy supplier’s supply simultaneously. By utilizing Armijo line search to instead of fixed step size of the dual subgradient method, the decentralized online RTP algorithm proposed in this research can overcome the defects of slow convergence and even no convergence from the original dual subgradient method. Finally, the simulation validates the rationality and feasibility of optimization model by the decentralized online RTP algorithm.
Dinh Hoa Nguyen - One of the best experts on this subject based on the ideXlab platform.
-
novel control approaches for demand response with real time Pricing using parallel and distributed consensus based admm
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Dinh Hoa Nguyen, Shun-ichi Azuma, Toshiharu SugieAbstract:This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power generating and consuming units can exchange information as a multiagent system (MAS), and a Real-Time Pricing (RTP) scheme is proposed. When the communication graph among agents is connected, a novel parallel and distributed consensus-based algorithm is proposed to derive an RTP scheme to facilitate DR, and when communication uncertainties exist, a robust consensus algorithm is proposed to cease the effect of uncertainties. Next, this paper proposes a novel control mechanism to tackle the problem of disconnected communication among agents, e.g., under cyber-attacks, by employing the so-called mixed communication-broadcast control architecture where the underlying ideas are twofold. First, each area in the grid associated with a connected subgraph is controlled by a MAS to guarantee the power balance and to reach consensus on the local electric price for that area. Second, a supervisory unit observes those local electric prices to calculate the global electric price for the whole grid and then broadcasts to all units so that they can properly adjust their output powers. Simulations are carried out on the IEEE 39-bus system to validate the proposed control mechanisms.
-
optimal demand response and real time Pricing by a sequential distributed consensus based admm approach
IEEE Transactions on Smart Grid, 2018Co-Authors: Dinh Hoa Nguyen, Tatsuo Narikiyo, Michihiro KawanishiAbstract:This paper proposes a novel optimization model and a novel approach to derive new demand response (DR) and Real-Time Pricing schemes for smart grid in which renewable energy and power losses are taken into account. In our proposed optimization model, a time-varying load constraint is introduced to better capture the consumption variation of customers and hence gives our approach an adaptive feature as well as facilitates DR. Then our approach enables all generation and demand units to actively collaborate in a distributed manner to obtain the optimal electric price and their optimal power updates in Real-Time while achieving their best profits. To do so, the total welfare in the grid is maximized and the optimization problem is analytically solved using the alternating direction method of multipliers and consensus theory for multi-agent systems. Moreover, the power balance constraint is guaranteed in every iteration of the proposed algorithm. Next, the effects of renewable energy to conventional generation, consumer consumption, and electric price are theoretically revealed which show the essential role of renewable energy for peak load shifting. Finally, simulations on the IEEE 39-bus system are introduced to illustrate the effectiveness of the proposed approach.
Robert Schober - One of the best experts on this subject based on the ideXlab platform.
-
Real-Time Pricing for Demand Response Based on Stochastic Approximation
IEEE Transactions on Smart Grid, 2014Co-Authors: Pedram Samadi, Vincent Wai Sum Wong, Hamed Mohsenian-rad, Robert SchoberAbstract:In this paper, we propose a new Pricing algorithm to minimize the peak-to-average ratio (PAR) in aggregate load demand. The key challenge that we seek to address is the energy provider's uncertainty about the impact of prices on users' load profiles, in particular when users are equipped with automated energy consumption scheduling (ECS) devices. We use an iterative stochastic approximation approach to design two Real-Time Pricing algorithms based on finite-difference and simultaneous perturbation methods, respectively. We also propose the use of a system simulator unit (SSU) that employs approximate dynamic programming to simulate the operation of the ECS devices and users' price-responsiveness. Simulation results show that our proposed Real-Time Pricing algorithms reduce the PAR in aggregate load and help the users to reduce their energy expenses.
-
optimal real time Pricing algorithm based on utility maximization for smart grid
International Conference on Smart Grid Communications, 2010Co-Authors: Pedram Samadi, Amirhamed Mohsenianrad, Robert Schober, Vincent Wai Sum WongAbstract:In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy Pricing as an essential tool to develop efficient demand side management strategies, we propose a novel Real-Time Pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the Real-Time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed Real-Time Pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
Behnam Mohammadiivatloo - One of the best experts on this subject based on the ideXlab platform.
-
optimal scheduling of electric vehicles and photovoltaic systems in residential complexes under real time Pricing mechanism
Journal of Cleaner Production, 2020Co-Authors: Sahar Seyyedeh Barhagh, Mehdi Abapour, Behnam MohammadiivatlooAbstract:Abstract Residential complexes (RCs) consisting of photovoltaic (PV) systems, wind turbines and electric vehicles (EVs), have been rapidly extended in energy systems in recent years. Optimal scheduling of local generation units can lead to economic improvement in RCs. In this regard, optimization of the performance of RCs appears to be necessary. Operators of RC energy systems equipped with local generation units can benefit from demand response programs (DRPs) to reduce their operating costs while satisfying energy demands. Within these programs, energy consumers are motivated to change their consumption in a way that economic targets are satisfied. In this paper, optimal operation of a grid-connected EV/PV RC energy system is studied under Real-Time Pricing of a DRP. The studied RC energy system is equipped with PV units and EVs that can support RCs in supplying energy demands and providing economic benefits. The incorporated PV unit is modeled considering solar irradiance parameters and ambient temperature, which can lead to accurate simulation results. Moreover, the proposed model for EVs can enhance the efficient operation of RCs through optimal charge and discharge processes. Further, the optimal operation of energy systems integrated into RCs is modeled as mixed-integer linear programming (MILP). Additionally, a general algebraic modeling system (GAMS) is used to carry out simulations in different scenarios and the results are presented for comparison. For the studied test case, the total expected cost of RCs is reduced by 37.31%, which represents the influential impact of demand response on the optimal scheduling of DG units.
Hongbo Zhu - One of the best experts on this subject based on the ideXlab platform.
-
a rolling penalty function algorithm of real time Pricing for smart microgrids based on bilevel programming
Engineering Optimization, 2020Co-Authors: Li Tao, Ying Liu, Yan Gao, Hongbo ZhuAbstract:A Real-Time Pricing scheme is formulated based on bilevel programming to tackle the uncertainties for smart microgrids equipped with renewable energy sources, dispatchable resources and storage dev...
-
multi time slots real time Pricing strategy with power fluctuation caused by operating continuity of smart home appliances
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Hongbo Zhu, Yan Gao, Yong Hou, Li TaoAbstract:Abstract Demand side management aims to match power demand to supply through cutting the peak and filling the valley, is one of the most important factors in smart grid. The Real-Time Pricing (RTP) mechanism is an ideal method to adjust power balance between supply and demand. Its implementation has a profound impact on users’ behavior, and on operation and management of the power grid. In this research, we propose an expectation social welfare maximization model, considering the classification of the smart home appliances (SHA) and the correlation of power consumption of multi-time slots. Users can arrange their appliances more profitable and more closely to reality with the advantage of multi-time slots RTP strategy. The constraint in the model reflects the fluctuation (uncertainty) of power consumption caused by operating continuity of the SHA. By introducing probabilistic constraints, the uncertainty optimization model is transformed into a convex optimization problem. The existence and uniqueness of the optimal solution are shown, and its properties are further analyzed. Considering the convex optimization problem is separable in dual domain, this study proposes a decentralized online RTP algorithm to determine each user’s demand and energy supplier’s supply simultaneously. By utilizing Armijo line search to instead of fixed step size of the dual subgradient method, the decentralized online RTP algorithm proposed in this research can overcome the defects of slow convergence and even no convergence from the original dual subgradient method. Finally, the simulation validates the rationality and feasibility of optimization model by the decentralized online RTP algorithm.
-
Real-Time Pricing for Demand Response in Smart Grid Based on Alternating Direction Method of Multipliers
Hindawi Limited, 2018Co-Authors: Hongbo Zhu, Yan Gao, Yong HouAbstract:The Real-Time Pricing (RTP) scheme is an ideal method to adjust the power balance between supply and demand in smart grid systems. This scheme has a profound impact on users’ behavior, system operation, and overall grid management in the electricity industry. In this research, we conduct an extended discussion of a RTP optimization model and give a theoretical analysis of the existence and uniqueness of the Lagrangian multiplier. A distributed optimization method based on the alternating direction method of multipliers (ADMM) algorithm with Gaussian back substitution (GBS) is proposed in this study. On the one hand, the proposed algorithm takes abundant advantage of the separability among variables in the model. On the other hand, the proposed algorithm can not only speed up the convergence rate to enhance the efficiency of computing, but also overcome the deficiency of the distributed dual subgradient algorithm, the possibility of nonconvergence in the iteration process. In addition, we give the theoretical proof of the convergence of the proposed algorithm. Furthermore, the interdependent relationship between variables has been discussed in depth during numerical simulations in the study. Compared with the dual subgradient method, the simulation results validate that the proposed algorithm has a higher convergence speed and better implementation effect
-
real time Pricing scheme based on stackelberg game in smart grid with multiple power retailers
Neurocomputing, 2017Co-Authors: Yeming Dai, Hongwei Gao, Ya Gao, Hongbo ZhuAbstract:Abstract As an essential characteristic of smart grid, demand response may reduce the power consumption of users and the operating expense of power suppliers. Real-Time Pricing is the key component of demand response that encourages power utilization in an efficient and economical way. In this paper, we study the Real-Time Pricing scheme in smart grid with multiple retailers and multiple residential users using Stackelberg game. Additionally, the price competition among power retailers is formulated as a non-cooperative game, while the coordination among the residential users is formulated as an evolutionary game considering the private information of power retailers and residential users. The existence of Stackelberg equilibrium is proved. Moreover, two special algorithms are developed to solve the equilibrium. Numerical results show the convergence of algorithms, and also confirm the efficiency and effectiveness of proposed Real-Time Pricing scheme.
