scholarly journals Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 936
Author(s):  
Jingjing Zhai ◽  
Xiaobei Wu ◽  
Zihao Li ◽  
Shaojie Zhu ◽  
Bo Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Programming Model ◽  
Energy System ◽  
Linearization Method ◽  
Mixed Integer ◽  
Operation Model ◽  
Piecewise Linearization ◽  
Constraints Network ◽  
Operation Scheduling

An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

scholarly journals Impact of demand response on BIPV and district multi-energy systems design in Singapore and Switzerland

2021 ◽  
Vol 2042 (1) ◽  
pp. 012096
Author(s):  
Christoph Waibel ◽  
Shanshan Hsieh ◽  
Arno Schlüter
Keyword(s):  
Demand Response ◽  
Programming Model ◽  
Systems Design ◽  
Energy Systems ◽  
Mixed Integer ◽  
Peak Shaving ◽  
Climate Conditions ◽  
Building Integrated Photovoltaics ◽  
Energy Hub ◽  
The Impact

Abstract This paper demonstrates the impact of demand response (DR) on optimal multi-energy systems (MES) design with building integrated photovoltaics (BIPV) on roofs and façades. Building loads and solar potentials are assessed using bottom-up models; the MES design is determined using a Mixed-Integer Linear Programming model (energy hub). A mixed-use district of 170,000 m2 floor area including office, residential, retail, education, etc. is studied under current and future climate conditions in Switzerland and Singapore. Our findings are consistent with previous studies, which indicate that DR generally leads to smaller system capacities due to peak shaving. We further show that in both the Swiss and Singapore context, cost and emissions of the MES can be reduced significantly with DR. Applying DR, the optimal area for BIPV placement increases only marginally for Singapore (~1%), whereas for Switzerland, the area is even reduced by 2-8%, depending on the carbon target. In conclusion, depending on the context, DR can have a noticeable impact on optimal MES and BIPV capacities and should thus be considered in the design of future, energy efficient districts.

Route selection for best distances in road databases based on drivers’ and customers’ preferences

2012 ◽  
Vol 42 (6) ◽  
pp. 1126-1140 ◽  
Author(s):  
P. Flisberg ◽  
B. Lidén ◽  
M. Rönnqvist ◽  
J. Selander
Keyword(s):  
Integer Programming ◽  
Shortest Path ◽  
Optimization Model ◽  
Programming Model ◽  
Mixed Integer ◽  
Route Selection ◽  
Speed Limits ◽  
Online System ◽  
Selection For ◽  
National Road

The importance of road databases for distance calculations and route selection is increasing. One reason is that payments and invoicing are often based on the distance driven. However, it can be hard to agree on a “best” distance because of drivers’ preferences. These preferences can be described by road features such as road length, quality, width, speed limits, etc. Moreover, a pure standard “shortest path”, which is often used in road databases, can result in a route that is considerably shorter than a preferred and agreed distance. Consequently, there is a need to find suitable weights for the features of the roads that provide fair and agreed distances at the same time for all users. We propose an approach to find values of such weights for the features. The optimization model to find weights is an inverse shortest path problem formulated in a mixed integer programming model. The approach is tested for the Swedish Forestry National Road database. Since 2010, it has been in daily use to establish distances and is available for all forestry companies and haulers in Sweden through an online system.

scholarly journals Optimal Operation of Soft Open Points-Based Energy Storage in Active Distribution Networks by Considering the Battery Lifetime

2021 ◽  
Vol 8 ◽  
Author(s):  
Jian Wang ◽  
Niancheng Zhou ◽  
Anqi Tao ◽  
Qianggang Wang
Keyword(s):  
Energy Storage ◽  
Reactive Power ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Operation Model ◽  
Battery Lifetime ◽  
Battery Degradation ◽  
Active Distribution Networks

Soft open point-based energy storage (SOP-based ES) can transfer power in time and space and also regulate reactive power. These characteristics help promote the integration of distributed generations (DGs) and reduce the operating cost of active distribution networks (ADNs). Therefore, this work proposed an optimal operation model for SOP-based ES in ADNs by considering the battery lifetime. First, the active and reactive power equations of SOP-based ES and battery degradation cost were modeled. Then, the optimal operation model that includes the operation cost of ADNs, loss cost, and battery degradation cost was established. The mixed integer nonlinear programming model was transformed to a mixed integer linear programming model derived through linearization treatment. Finally, the feasibility and effectiveness of the proposed optimization model are verified by the IEEE33 node system.

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

scholarly journals Generation Expansion Planning Model for Integrated Energy System Considering Feasible Operation Region and Generation Efficiency of Combined Heat and Power

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 226 ◽  
Author(s):  
Woong Ko ◽  
Jinho Kim
Keyword(s):  
Optimization Model ◽  
Energy System ◽  
Combined Heat And Power ◽  
Mixed Integer ◽  
Planning Model ◽  
Generation Expansion Planning ◽  
Generation Efficiency ◽  
Expansion Planning ◽  
Generation Expansion ◽  
Integrated Energy System

Integrated energy systems can provide a more efficient supply than individual systems by using resources such as cogeneration. To foster efficient management of these systems, the flexible operation of cogeneration resources should be considered for the generation expansion planning model to satisfy the varying demand of energy including heat and electricity, which are interdependent and present different seasonal characteristics. We propose an optimization model of the generation expansion planning for an integrated energy system considering the feasible operation region and efficiency of a combined heat and power (CHP) resource. The proposed model is formulated as a mixed integer linear programming problem to minimize the sum of the annualized cost of the integrated energy system. Then, we set linear constraints of energy resources and describe linearized constraints of a feasible operation region and a generation efficiency of the CHP resource for application to the problem. The effectiveness of the proposed optimization problem is verified through a case study comparing with results of a conventional optimization model that uses constant heat-to-power ratio and generation efficiency of the CHP resource. Furthermore, we evaluate planning schedules and total generation efficiency profiles of the CHP resource for the compared optimization models.

scholarly journals Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2409 ◽  
Author(s):  
Arslan Bashir ◽  
Mahdi Pourakbari Kasmaei ◽  
Amir Safdarian ◽  
Matti Lehtonen
Keyword(s):  
Demand Response ◽  
Programming Model ◽  
Residential Building ◽  
Hot Water ◽  
Mixed Integer ◽  
Efficient Utilization ◽  
Space Cooling ◽  
Pv Generation ◽  
Heating Loads ◽  
And Storage

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

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

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2448 ◽  
Author(s):  
Yining Zhang ◽  
Yubin He ◽  
Mingyu Yan ◽  
Chuangxin Guo ◽  
Yi Ding
Keyword(s):  
Natural Gas ◽  
Demand Response ◽  
Demand Curve ◽  
Stochastic Scheduling ◽  
Linearization Method ◽  
Piecewise Linearization ◽  
Interconnected System ◽  
Approximation Accuracy ◽  
Scheduling Model ◽  
Upper Level

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.

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