Capacity planning and optimization of business park-level integrated energy system based on investment constraints

Wang, Yongli; Li, Ruiwen; Dong, Huanran; Ma, Yuze; Yang, Jiale; Zhang, Fuwei; Zhu, Jianqin; Liu, Shuqing

doi:10.1016/j.energy.2019.116345

Cited by 76 publications

(24 citation statements)

References 21 publications

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“…Yongli Wang et al [19] established a novel multi-objective optimization model for the design of integrated energy system with electric, thermal and cooling subsystem to simultaneously minimize the economic, technical and environmental objectives. Aiming at the integrated energy system formed by multi-energy coupling, Yongli Wang et al [20] adopted three investment restraint schemes, simulated the economic operation of the system based on typical daily load characteristic curves in different seasons, and established an optimal capacity allocation model of the integrated energy system, which took into account the investment cost restraint and minimizing the total annual cost and carbon dioxide emissions. L. Yu et al [21] developed an interval possibilistic-stochastic programming (IPSP) method that can deal with multiple uncertainties existed in the real-world mixed energy system (MES).…”

Section: Literature Reviewmentioning

confidence: 99%

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Wang

Zhou

Dong

et al. 2020

Preprint

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BackgroundWith the rapid development of social economy, energy consumption has continued to grow, and the contradiction between energy supply and demand has become increasingly tense. Cities account for two-thirds of global primary energy demand that makes urban energy systems become a center of sustainable transitions.MethodsThis paper builds a bi-level planning model for steady state optimal configuration to realize the reasonable planning of the urban energy structure. The first level mainly analyzes the relationship between coal, petroleum, natural gas, and renewable energy when the energy system is in a stable state. Based on the steady-state relationship of multiple energy sources, the second level is mainly aiming at energy allocation of the city, to achieve the minimization of construction and operating costs of the urban energy system, as well as the pollutant emissions. And nonlinear system dynamics and the non-dominated sorting genetic algorithm II (NSGA-II) algorithm are implemented to solve the model.ResultsThe model studies the interaction between four energy sources to find out the demand of each energy source when the urban energy system is in a stable state. To illustrate the advantage of the method, the application of the planning model proposed in this paper is discussed in the energy system of city J of China. Under the premise of ensuring the stability of the urban energy system, the city J has two energy planning schemes: mainly coal or mainly high-quality energy.ConclusionThe coal-based energy structure can save economic costs. However, from the perspective of actual demand or ecological environment, the urbanization level of city J is relatively high, and the coal-based energy structure is unreasonable and may cause greater environmental pollution. Under the type of high-quality energy development model, city J should continue to strengthen the adjustment of the industrial structure and optimize and upgrade the urban development path. The focus is on strengthening the use of clean energy and improving energy efficiency, thereby further reducing the demand for traditional fossil energy in the process of economic development.

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Section: Literature Reviewmentioning

confidence: 99%

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Wang

Zhou

Dong

et al. 2020

Preprint

Self Cite

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“…The lower layer is a single-objective nonlinear operation optimization model, some of which are solved directly by intelligent algorithms [12], and others convert nonconvex into convex [13], using mathematical methods [11] or solvers [14] to solve. Some references further consider the uncertainty of the system, and use robust and multi-scenario methods for planning [15].…”

Section: Introductionmentioning

confidence: 99%

“…In view of the flexibility of IES operation, most references at this stage adopt the modelling method of integration of operation and planning [10]. The upper layer is a multi-objective nonlinear equipment optimization configuration model, and intelligent algorithms are often adopted for this layer, such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) [11].…”

Section: Introductionmentioning

confidence: 99%

“…The upper layer is a multi-objective nonlinear equipment optimization configuration model, and intelligent algorithms are often adopted for this layer, such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) [11]. The lower layer is a single-objective nonlinear operation optimization model, some of which are solved directly by intelligent algorithms [12], and others convert nonconvex into convex [13], using mathematical methods [11] or solvers [14] to solve. Some references further consider the uncertainty of the system, and use robust and multi-scenario methods for planning [15].…”

Section: Introductionmentioning

confidence: 99%

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Optimal device capacity planning and strategy determination in an integrated energy system to promoting IES integration considering reliability value

Xueqian

Liu

2021

IET Generation Trans & Dist

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This paper proposes a novel framework for optimal planning of integrated energy system (IES) that takes into account its reliability value, which aims at promoting the power grid and IES to share the responsibility of the user's energy-supply reliability by market mechanism, thus promoting IES integration. First, an IES grid-connected electricity purchase price estimation model considering reliability is established, based on price theory and reliability theory, to quantify the reliability incremental value of IES. Then, a two-layer collaborative planning model, containing optimal equipment capacity configuration and strategy determination in the upper layer and optimal operation simulation and IES incremental reliability value in the lower layer, is established to coordinate the reliability guarantee capability of distribution network and IES while maximize the profit of IES. Among them, a practical approach of IES probabilistic reliability calculation is studied, considering double uncertainty and time-series matching of generation and demand, aiming at reducing the complexity of electricity price calculation embedded in planning model. Next, the intelligent krill herd algorithm and MILP are implemented to solve the model. Finally, a case is studied to verify the effectiveness of the model, and analyse the impact of load growth rate and user reliability requirements on the planning results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Through the optimal selection of planning schemes, the installed capacity of renewable energy can be maximised to reduce the system's energy consumption and environmental cost. A model combining equipmentinvestment constraints and capacity planning was established in [10] to realise the minimum annual total cost and carbon dioxide emissions under the constraint of limited funds. A planning model for the PIES in the data centre was proposed in [11], and the Markov-based reliabilityestimation method was adopted to carry out redundant design of equipment capacity to improve PIES's reliability.…”

Section: Introductionmentioning

confidence: 99%

Multi‐scene upgrade and renovation method of existing park‐level integrated energy system based on comprehensive analysis

Cao

Jia

et al. 2020

Energy Conversion and Economics

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Some of the existing park‐level integrated energy systems (PIESs) experience problems such as mismatch between energy supply and demand, high operation costs, and low renewable energy consumption capacity. To improve the energy supply level of the existing PIES, the multi‐scene upgrade and renovation method of the existing PIES based on comprehensive analysis is proposed. First, the physical model of the existing PIES for renovation is constructed, including expansion of existing equipment and newly added candidate equipment. Second, to determine the deficiencies, the current situation of the existing PIES is analysed based on the planning and operation data as well as indexes including economy, photovoltaic consumption capacity, and so on. On this basis, multiple renovation scenes are set up, and the upgrade and renovation model is constructed, assuming minimal investment, operation, and maintenance costs to be the objective function. Thereafter, the equipment allocation scheme of each scene is solved, for which comprehensive evaluation is performed. Finally, considering an existing PIES as an example, the weak energy storage and heating links are upgraded and renovated, effectively improving the performance of the existing PIES in reducing costs and carbon emissions, and improving energy utilisation efficiency.

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Capacity planning and optimization of business park-level integrated energy system based on investment constraints

Cited by 76 publications

References 21 publications

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Optimal device capacity planning and strategy determination in an integrated energy system to promoting IES integration considering reliability value

Multi‐scene upgrade and renovation method of existing park‐level integrated energy system based on comprehensive analysis

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