scholarly journals Location planning of distributed energy stations based on improved P-median model

2021 ◽  
Vol 256 ◽  
pp. 02009
Author(s):  
Zhengji Meng ◽  
Xiaoguang Hao ◽  
Shiyan Liu ◽  
Jianfeng Li
Keyword(s):  
Energy Demand ◽  
Energy Use ◽  
Transportation Network ◽  
Effective Means ◽  
Energy System ◽  
Weight Coefficient ◽  
Distributed Energy ◽  
Increase Energy Efficiency ◽  
Integrated Energy Systems ◽  
Integrated Energy System

The integrated energy system creates the possibility for the interconnection and coordination of different energy sources, and is an effective means to improve the energy use of the system, increase energy efficiency, and reduce environmental pollution. At present, the planning of distributed energy stations for integrated energy systems mostly focuses on equipment selection and equipment capacity. However, there are relatively few studies on the location of energy stations and pipeline layout planning. Firstly, this paper proposes a distributed energy station location method based on the improved p-median model, which combines the energy supply path of the energy station with the actual transportation network, and introduces the weight coefficient of multi energy load to reflect the diversity of energy demand of load. Finally, the specific solution method is given, and the rationality and feasibility of the proposed method are verified by an example.

scholarly journals Optimal Planning of Integrated Energy Systems Based on Coupled CCHP

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2621 ◽  
Author(s):  
Xiaofeng Dong ◽  
Chao Quan ◽  
Tong Jiang
Keyword(s):  
Power Generation ◽  
Energy Use ◽  
Clean Energy ◽  
Energy System ◽  
Power Lines ◽  
Gas Pipelines ◽  
Integrated Energy Systems ◽  
Integrated Energy System ◽  
Gas Price ◽  
Combined Cooling

With the widespread attention on clean energy use and energy efficiency, the integrated energy system (IES) has received considerable research and development. This paper proposed an electricity-gas IES optimization planning model based on a coupled combined cooling heating and power system (CCHP). The planning and operation of power lines and gas pipelines are considered. Regarding CCHP as the coupled hub of an electricity-gas system, the proposed model minimizes total cost in IES, with multistage planning and multi-scene analyzing. Renewable energy generation is also considered, including wind power generation and photovoltaic power generation. The numerical results reveal the replacing and adding schemes of power lines and gas pipelines, the optimal location and capacity of CCHP. In comparison with conventional separation production (SP), the optimization model which regards CCHP as the coupled hub attains better economy. At the same time, the influence of electricity price and natural gas price on the quantities of purchasing electricity and purchasing gas in the CCHP system is analyzed. According to the simulation result, a benchmark gas price is proposed, which shows whether the CCHP system chooses power generation. The model results and discussion demonstrate the validity of the model.

scholarly journals Integrated energy system planning research based on big data load prediction method

2021 ◽  
Vol 267 ◽  
pp. 01005
Author(s):  
Yongli Wang ◽  
Hekun Shen ◽  
Jialin Yang ◽  
Nan Wang ◽  
Yuze Ma ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Demand ◽  
Energy Use ◽  
Prediction Method ◽  
Energy System ◽  
System Capacity ◽  
Prediction Methods ◽  
Load Prediction ◽  
Related Factors ◽  
Integrated Energy System

The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.

Regional integrated energy system schemes selection based on risk expectation and Mahalanobis-Taguchi system

2021 ◽  
pp. 1-18
Author(s):  
Jiahang Yuan ◽  
Yun Li ◽  
Xinggang Luo ◽  
Lingfei Li ◽  
Zhongliang Zhang ◽  
...  
Keyword(s):  
Energy Demand ◽  
Choquet Integral ◽  
Subjective Evaluation ◽  
Risk Attitude ◽  
Energy System ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Energy Structure ◽  
Fuzzy Measures ◽  
Integrated Energy System

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

scholarly journals Optimal Design on Fossil-to-Renewable Energy Transition of Regional Integrated Energy Systems under CO2 Emission Abatement Control: A Case Study in Dalian, China

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2879
Author(s):  
Xinxin Liu ◽  
Nan Li ◽  
Feng Liu ◽  
Hailin Mu ◽  
Longxi Li ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Optimal Design ◽  
Co2 Emission ◽  
Energy Demand ◽  
Energy Systems ◽  
Energy System ◽  
Mixed Integer ◽  
Total Annual Cost ◽  
Emission Abatement ◽  
Integrated Energy Systems

Optimal design of regional integrated energy systems (RIES) offers great potential for better managing energy sources, lower costs and reducing environmental impact. To capture the transition process from fossil fuel to renewable energy, a flexible RIES, including the traditional energy system (TES) based on the coal and biomass based distributed energy system (BDES), was designed to meet a regional multiple energy demand. In this paper, we analyze multiple scenarios based on a new rural community in Dalian (China) to capture the relationship among the energy supply cost, increased share of biomass, system configuration transformation, and renewable subsidy according to regional CO2 emission abatement control targets. A mixed integer linear programming (MILP) model was developed to find the optimal solutions. The results indicated that a 40.58% increase in the share of biomass in the RIES was the most cost-effective way as compared to the separate TES and BDES. Based on the RIES with minimal cost, by setting a CO2 emission reduction control within 40%, the RIES could ensure a competitive total annual cost as compared to the TES. In addition, when the reduction control exceeds 40%, a subsidy of 53.83 to 261.26 RMB/t of biomass would be needed to cover the extra cost to further increase the share of biomass resource and decrease the CO2 emission.

scholarly journals Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tianhe Sun ◽  
Tieyan Zhang ◽  
Yun Teng ◽  
Zhe Chen ◽  
Jiakun Fang
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Energy System ◽  
Small Scale ◽  
Power Demand ◽  
Distributed Energy ◽  
Strategic Competition ◽  
Integrated Energy System ◽  
Monthly Electricity Consumption

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

scholarly journals Operational Risk Assessment of Electric-Gas Integrated Energy Systems Considering N-1 Accidents

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1208 ◽  
Author(s):  
Hua Liu ◽  
Yong Li ◽  
Yijia Cao ◽  
Zilong Zeng ◽  
Denis Sidorov
Keyword(s):  
Risk Assessment ◽  
Energy System ◽  
Assessment System ◽  
Coupled Systems ◽  
Assessment Model ◽  
Analysis Model ◽  
Integrated Energy Systems ◽  
Integrated Energy System ◽  
Operation Status ◽  
The Mathematical Model

The reliability analysis method and risk assessment model for the traditional single network no longer meet the requirements of the risk analysis of coupled systems. This paper establishes a risk assessment system of electric-gas integrated energy system (EGIES) considering the risk security of components. According to the mathematical model of each component, the EGIES steady state analysis model considering the operation constraints is established to analyze the operation status of each component. Then the EGIES component accident set is established to simulate the accident consequences caused by the failure of each component to EGIES. Furthermore, EGIES risk assessment system is constructed to identify the vulnerability of EGIES components. Finally, the risk assessment of IEEE14-NG15 system is carried out. The simulation results verify the effectiveness of the proposed method.

Towards a 3D Spatial Urban Energy Modelling Approach

2014 ◽  
Vol 3 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jean-Marie Bahu ◽  
Andreas Koch ◽  
Enrique Kremers ◽  
Syed Monjur Murshed
Keyword(s):  
High Resolution ◽  
Distributed Generation ◽  
Energy Demand ◽  
Energy Use ◽  
Energy Systems ◽  
Energy System ◽  
Heat Energy ◽  
3D City Models ◽  
City Models ◽  
Modelling Approach

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

scholarly journals Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Deng ◽  
Yixin Huang ◽  
Yuge Chen ◽  
Changming Chen ◽  
Li Yang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Dynamic Characteristics ◽  
Energy System ◽  
Integrated Energy Systems ◽  
Equipment Construction ◽  
Physical Energy ◽  
Proposed Model ◽  
Integrated Energy System ◽  
Node Network

The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

scholarly journals Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1355 ◽  
Author(s):  
Linjuan Zhang ◽  
Jiaqi Shi ◽  
Lili Wang ◽  
Changqing Xu
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Energy System ◽  
Multitask Learning ◽  
Effective Characteristics ◽  
Industrial Park ◽  
Energy Forecasting ◽  
Training Stage ◽  
Integrated Energy System ◽  
Term Energy

Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.

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