Energy station and distribution network collaborative planning of integrated energy system based on operation optimization and demand response

This paper proposes a novel structure to optimize the operational strategies of responsive farms for day-ahead peak shaving. To achieve the aim, the modern irrigation system of farms, including groundwater, surface water, and booster water pumps, are modeled mathematically. To develop the demand response (DR) potentials of the farms, electrical storage systems, and self-generation facilities, including thermal distributed generations and on-farm solar sites, are addressed. In order to facilitate the integration of the agricultural DR programs into the electricity market, a mathematical formulation for agricultural demand response aggregator (ADRA) is suggested. The ADRA participates in the dayahead electricity market on behalf of the responsive farms. To overcome the price uncertainty of the electricity market, a robust optimization approach is addressed. This approach determines the robust decisions of farms in the worstcase realizations of the uncertain electricity price. Regarding on-farm solar sites located in rural areas, a data-driven approach is used to estimate the solar power generation of a significant number of sites without needing to install costly communication and measurement infrastructures. Finally, the proposed approach is implemented on agricultural lands in the northeast of Iran. The numerical results show that the suggested approach provides day-ahead peak shaving for the power systems meeting the crop's water requirements.

Section: Description Of Problem and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

Golmohamadi

2020

“…For instance, Feng et al proposed a heat transfer based model of the combined cooling, heating, and power IES (CCHP‐IES) to compare the cooling supply modes and operation strategies 6 . Similarly, Liu established a model in the forms of heat constraints of heat‐supplied devices to study the collaborative planning of IES 7 . Still not considering detailed thermal processes, a bi‐level model was proposed by Lu et al 8 Considerable studies have been carried out based on such a modeling idea that oversimplifies the process variables 9‐11 .…”

Section: Introductionmentioning

confidence: 99%

Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

Wei

Sun

et al. 2020

Summary A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

“…Recent power networks are developed through a combination of several renewable energy resources (RESs) and energy storage systems as hybrid systems that have gained the attention of researchers in recent study 1‐4 . Tidal turbine generator (TTG) is a major resource of RESs that uses the ocean and sea energy, and shows much promising improvement to the resilience of the energy sector 5‐7 .…”

Section: Introductionmentioning

confidence: 99%

Enhancement of tidal generators by superconducting energy storage and Jaya‐based sliding‐mode controller

Othman

2020

This article presents a novel control technique of Jaya-based super-sliding controller that is applied on superconducting magnetic energy storage system (SCMES). The SCMES will be integrated to operate with tidal turbine generators (TTGs). The penetration of TTGs has recently grown due to distributed generation contributions to the energy sector. To alleviate the potential system instability due to TTG dynamic performance, SCMES is connected to TTG to support the dynamic operational conditions. SCMES is implemented to act quickly during both cases of power generation and absorption, so it can realize any required mismatch between the supply and load power at dynamic transient periods. To enhance such capability, this article proposes the application of Jaya-based super-sliding control technique to control and tune the system dynamic gains. The proposed technique implements incorporated cooperative algorithms using enhanced Jaya competitive algorithm and super-sliding method to facilitate the exchange of energy between the SCMES, the TTG, and the power network. It allows smooth dynamic performance, power quality enhancement, and robust dynamic reference tracking. Jaya-based super-sliding controller is developed and proposed for accurate and reliable control and tunning of system parameters. The proposed controller ensures the superb performance of the synergy of SCMESs and TTGs. Digital simulations have validated the effectiveness of the proposed system with the optimal dynamic parameters and minimization of the global control error.