Optimal Power Demand Research Articles

A cloud-based energy management strategy for hybrid electric city bus considering real-time passenger load prediction

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers. After analyzing the importance of battery aging and passenger load effects on an optimal energy management strategy, this study introduces the passenger load prediction into the hybrid-electric city buses energy management problem, which is not well studied in the existing literature. The average model, Decision Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, a dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage leveraging cloud techniques. Then, rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to the vehicle onboard controller to handle prediction errors and uncertainties. The proposed cloud-based Dynamic Programming and rule extraction framework with the passenger load prediction show 4% and 11% lower bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% of the dynamic programming with the true passenger load information.

A hybrid demand response mechanism based on real-time incentive and real-time pricing

This paper proposes a hybrid demand response (DR) mechanism considering three types of participants: power grid operator (PGO), retailers and end users. Different from the traditional price-based or incentive-based methods, this hybrid mechanism combines real-time pricing and real-time incentive together to implement demand response programs dispatched by PGO, i.e., the PGO provides incentives to retailers and the retailers set optimal real-time prices to users every 5 min. This hybrid DR mechanism can better motivate retailers to participate by providing them with monetary incentives from PGO for load shifting. We use a three-level Stackelberg game to model the proposed mechanism. The PGO first determines the optimal incentive rate to minimize its cost, then the retailers decide the optimal electricity price to maximize their profits, and the users finally choose the optimal power demand to maximize their welfare. The analytical solutions of the optimal decisions for every participant are given. We also propose a distributed algorithm to implement this mechanism in a practical application by considering information asymmetry. The simulation results verify its advantages over traditional demand response mechanisms.

Cooperative Power Split Optimization for a Group of Intelligent Electric Vehicles Travelling on a Highway With Varying Slopes

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption.

Optimization scheduling model and method for Wind-PV-Pumped joint operation in high proportion renewable energy base

Aiming at the uncertainty of wind and PV output and the fluctuation of wind and PV grid connection output, an optimized scheduling model and method for wind-PV-pumped storage combined operation in high proportion renewable energy base is proposed. Firstly, the optimal scheduling model for 96 periods of the whole day was established with the objective of optimal comprehensive benefit, minimum power fluctuation and optimal power demand matching of the wind-PV-pumped storage co-generation system.Then, an NSGA-III algorithm more suitable for high-dimensional multi-objective optimization is introduced to obtain Pareto optimal solution set of co-generation system. Finally, the simulation of jiuquan new energy base was carried out to verify the effectiveness of the proposed model.

Optimal power demand management for cluster-level commercial buildings using the game theoretic method

Power demand management, particularly demand response controls, is the effort from the demand side to help the supply side management for helping maintain grid balance and also bring the economic benefits for building owners. Most of building demand management and demand response control strategies for commercial buildings only focus on a single building. However, as for the cluster-level buildings, which are sometimes involved in an account for electricity charge and also the main concern of smart grids, such conventional individual-level control strategies will not be effective. A game theory-based decentralized control strategy is therefore developed for cluster-level building demand management by simultaneously optimizing the indoor air temperature set-point and the operation of active thermal storage. Without gathering all the required information of buildings to a central optimization system, the cluster-level building demand management can be realized in a decentralized way. Case studies are conducted and results show that the proposed decentralized control strategy can increase the peak demand reduction, over two times than that when the demand management of buildings is conducted in an uncoordinated mode. Meanwhile, the performance of the proposed decentralized control strategy is closely approached to the results that are optimized in a centralized mode.

Power regulation of upstream wind turbines for power increase in a wind farm

A strategy to increase the power output from a wind farm by regulating the most upstream wind turbine to a lower power than its available power is investigated in this study. A similar concept but using a fixed pitch angle of the most upstream wind turbine available in the literature was extended to use power demands from a wind farm controller, and the effect of wind turbine spacing and the wind speed on the power increase were analyzed. From various simulations of a virtual wind farm consisting of ten 5MW wind turbines in series using a time-domain wind farm simulation tool, the optimal power demand from the wind farm controller to increase the total power output from the wind farm was investigated. When the wind turbine spacing is 4D, the increase in the total power output from the wind farm reached 4.1% when the partialization is about 0.825. It was found from the study that the optimal power demand varies with the wind turbine spacing. It was also found that for the same wind turbine spacing, the optimal power demand is about the same if the wind speed is below the rated wind speed and in the max-CP region. If the wind speed becomes higher and it is in the transition region or above the rated wind speed, the effect of the partialization to increase the total power decreased substantially.

マッチング理論に基づくアグリゲータによるメカニズムデザインを用いた電力需要管理

マッチング理論に基づくアグリゲータによるメカニズムデザインを用いた電力需要管理

Power Allocation in Primary User-Assisted Multi-Channel Cognitive Radio Networks

This paper addresses power allocation problem for spectrum sharing multi-band cognitive radio networks, where the primary user (PU) allows secondary users (SUs) to transmit simultaneously with it by coding SU's signal together with its own signal. The PU acts as the relay for the SUs and sells its transmit power to the SUs to increase its benefit, and the SUs bid for the PU's transmit power for maximizing their utilities. We propose a power allocation scheme based on traditional ascending clock auction, in which the SUs iteratively submit the optimal power demand to the PU according to the PU's announced price, and the PU updates that price based on all SUs' total power demands. Then we mathematically prove the convergence property of the proposed auction algorithm (i.e., the auction algorithm converges in a finite number of clocks), and show that the proposed power auction algorithm can maximize the social welfare. Finally, the performance of the proposed scheme is verified by the simulation results.

An Auction Mechanism for Power Allocation in Multi-Source Multi-Relay Cooperative Wireless Networks

In this paper, power allocation for multi-source multi-relay cooperative wireless networks is considered. An ascending-clock auction algorithm is proposed to efficiently allocate cooperative relay power among multiple source nodes in a distributed fashion. In particular, each source node reports its optimal power demand to each relay node based on the relays' announced prices. It is proven that the proposed auction algorithm enforces truthful power demands and converges in a finite number of time-steps to the unique Walrasian Equilibrium allocation that maximizes the sum of utilities. Numerical results are presented to supplement the theoretical analysis and demonstrate the efficiency of the proposed distributed relay power allocation algorithm.