Load carrying capability of regional electricity-heat energy systems:Definitions, characteristics, and optimal value evaluation

Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method.</p

Load carrying capability of regional electricity-heat energy systems:Definitions, characteristics, and optimal value evaluation

Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method.</p

Distributionally robust optimization for peer-to-peer energy trading considering data-driven ambiguity sets

Peer-to-peer (P2P) energy trading provides potential economic benefits to prosumers. The prosumers are responsible for managing their own resources/reserves within the energy community, especially for photovoltaic (PV). However, the intermittency of PV leaves a major issue for the optimal operation of P2P energy trading. This paper proposes a fully data-driven distributionally robust optimization (DRO) for P2P energy trading. Specifically, both the optimization approach and the ambiguity set of DRO are formed in a data-driven fashion. The proposed formulation minimizes the expected operation cost of each prosumer, which is modeled as a DRO problem considering the operational constraints. A decentralized energy negotiation mechanism and market clearing algorithm are proposed for P2P energy trading based on the alternating direction multiplier method. Furthermore, the ambiguity set is formed by deep Gaussian process under the framework of bootstrap aggregating. Finally, the equivalent linear programming reformulations of the proposed DRO model are carried out and solved in a distributed manner. Numerical results demonstrate that the proposed DRO-based approach has superior performance for handling the randomness of PV generation compared with robust optimization, stochastic programming, and other DRO variants

A direct investigation of photocharge transfer across monomolecular layer between C60 and CdS quantum dots by photoassisted conductive atomic force microscopy

The composite assembly of C60 and CdS Quantum Dots (QDs) on ITO substrate was prepared by Langmuir-Blodgett (LB) technique using arachic acid (AA), stearic acid (SA) and octadecanyl amine (OA) as additives. Photoassisted conductive atomic force microscopy was used to make point contact current-voltage (I-V) measurements on both the CdS QDs and the composite assembly of C60/CdS. The result make it clear that the CdS, C60/CdS assemblies deposited on ITO substrate showed linear characteristics and the current increased largely under illumination comparing with that in the dark. The coherent, nonresonant tunneling mechanism was used to explain the current occurrence. It is considered that the photoinduced carriers CdS QDs tunneled through alkyl chains increased the current rapidly