A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles

Concentrating solar power (CSP) station is counted as a promising flexible power supply when the net load power curve is duck-shaped in high photovoltaic (PV) penetration power system, which may lead to the serious phenomenon of PV curtailment and a large-capacity power shortage. This paper presents a mitigation strategy that replaces thermal power station with CSP station to participate in the optimal operation of power system for solving the duck-shaped net load power curve problem. The proposed strategy utilizes the dispatchability of thermal storage system (TSS) and the fast output regulation of unit in the CSP station. Simultaneously, considering the operation constraints of CSP station and network security constraints of the system, an optimization model is developed to minimize the overall cost including operation and penalty. The results obtained by nonlinear optimization function demonstrate that the replacement of concentrating solar power (CSP) station contributes to reducing the PV curtailment and lost load, while increasing the available equivalent slope for power balance. Thus, the proposed mitigation strategy can promote the penetration of PV generation and improve the flexibility of power system.

“…Equations (23) and (24) are the capacity and ramp constraint of the steam turbine unit in the TPS. The form is the same as Equations (12) and (13), but the parameters are different.…”

Section: Constraintsmentioning

confidence: 99%

Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station

Wang

Chang

Bai³

et al. 2019

“…The original system data are available in Reference [10]. The test system was partitioned into three microgrids, as shown in Figure 3.…”

Section: Simulation Datamentioning

confidence: 99%

Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method

Feng

Wen

Zhang³

et al. 2018

Self Cite

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM.Energies 2018, 11, 2555 2 of 18 for implementing the control center as well as communication infrastructures [3,4]. Furthermore, storing all data in one control center carries the risk of exposing the privacy of customers, as well as unavoidable single-point failures [3]. More importantly, the distributed method is more computationally efficient than the centralized one. Therefore, it is desirable to effectively coordinate NMs in a distributed manner for improving reliability and economics. Literature ReviewConventionally, a distribution-level microgrid is centrally controlled by a central coordination center [5]. More specifically, the central coordination center collects relevant information from dispersed controllable devices and forecasting data to perform an optimal dispatch of distributed resources for the next period [6]. Centralized energy-management architecture has been widely studied in existing publications. Reference [7] proposed a two-layer energy-management model wherein the schedule level attains the economic-operation scheme based on forecasts, while the dispatch level dispatches controllable DGs based on real-time data. In Reference [8], a centralized scheduling algorithm was proposed for an electric vehicle-dominated microgrid to optimize the charging scheme considering the charging cost and convenience of microgrid users. In Reference [9], the centralized energy-management problem for a household-level microgrid was formulated as a utility-maximization problem, subject to capacity constraints. In Reference [10], a centralized energy-management optimization model was formulated for a residential-quarter microgrid including a concentrating solar-power unit with an objective of minimizing the involved operation costs. A two-stage stochastic demand-side management model for a commercial-building microgrid is formulated in Reference [11], considering the uncertainties in solar-generation outputs, loads, microgrid availabilities, and microgrid energy demands.The fully distributed optimization method includes two broad categories, i.e., the Lagrangian relaxation-based and the optimality-condition decomposition-based category. The Lagrangian r...

“…(3) Natural gas network constraints Natural gas network is restrained by constraints (14)- (20). Constraints (14) and (15) represent the limits on gas well production and nodal pressure. Natural gas flow is modeled by the nonlinear Weymouth function as constraint (16), which is determined by the incremental pressure between two end nodes of pipeline, and Q s mn,t = (Q out,s mn,t + Q in,s mn,t )/2 is the average gas flow of pipeline mn.…”

Section: Transmission System Constraints Of Interconnected Ehsmentioning

confidence: 99%

Linearized Stochastic Scheduling of Interconnected Energy Hubs Considering Integrated Demand Response and Wind Uncertainty

Zhang

Yan

et al. 2018

In the context of the Energy Internet, customers are supplied by energy hubs (EH), while the EHs are interconnected through an upper-level transmission system. In this paper, a stochastic scheduling model is proposed for the interconnected EHs considering integrated demand response (DR) and wind variation. The whole integrated energy system (IES) is linearly modeled for the first time. The output-input relationship within the energy hub is denoted as a linearized matrix, while the upper-level power and natural gas transmission systems are analyzed through piecewise linearization method. A novel sequential linearization method is further proposed to balance computational efficiency and approximation accuracy. Integrated demand response is introduced to smooth out demand curve, considering both internal DR achieved by the optimal energy conversion strategy within energy hubs, and external DR achieved by demand adjustment on the customer’s side. Distributed energy storage like natural gas and heat storage are considered to provide buffer for system operation. The proposed stochastic model is solved by scenario-based optimization with a backward scenario reduction strategy. Numerical tests on a three-hub and seventeen-hub interconnected system that validates the effectiveness of the proposed scheduling model and solution methodology.