scholarly journals Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2739
Author(s):  
Mahtab Kaffash ◽  
Glenn Ceusters ◽  
Geert Deconinck
Keyword(s):  
Optimal Schedule ◽  
Measurement Data ◽  
Energy System ◽  
Optimal Operation ◽  
Probabilistic Forecast ◽  
Mixed Integer ◽  
Interval Optimization ◽  
Energy Storage Devices ◽  
Pv System ◽  
Point Forecast

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibration of probabilistic forecast is improved by 10% compared to building distributions around point forecast. Moreover, the results of interval optimization show that the uncertainty of the PV system not only has an influence on the electrical part of the MES, but also causes a shift in the behavior of the thermal system.

scholarly journals AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes

2021 ◽  
Vol 9 ◽  
Author(s):  
Andreas Kämper ◽  
Alexander Holtwerth ◽  
Ludger Leenders ◽  
André Bardow
Keyword(s):  
Measurement Data ◽  
Energy Systems ◽  
Optimal Operation ◽  
Data Driven ◽  
Mixed Integer ◽  
Model Generation ◽  
Global Optimality ◽  
Computationally Efficient ◽  
Model Quality ◽  
Pump System

The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields Mixed-Integer Linear Programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model.

scholarly journals Techno-Economic and Environmental Assessment of the Hybrid Energy System Considering Electric and Thermal Loads

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3136
Author(s):  
Li-Ning Xing ◽  
Hong-Long Xu ◽  
Armin Kardan Sani ◽  
Md. Alamgir Hossain ◽  
S. M. Muyeen
Keyword(s):  
Greenhouse Gas Emission ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Optimal Operation ◽  
System Structure ◽  
Pv System ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Cost Of Energy ◽  
Electric Loads

Optimal sizing of hybrid energy systems has been considerably investigated in previous studies. Nevertheless, most studies only focused on providing AC electric loads by renewable energy sources (RESs) and energy storage systems (ESSs). In this paper, a hybrid energy system, including photovoltaic (PV) system, ESS, fuel cell (FC), natural gas (NG) boiler, thermal load controller (TLC), and converter is optimized for supplying different load demands. Three scenarios are introduced to investigate the feasibility of the energy system. Environmental aspects of each system are analyzed, as there are NG-consuming sources in the system structure. A sensitivity analysis is conducted on the influential parameters of the system, such as inflation rate and interest rate. Simulation results show that the proposed hybrid energy system is economically and technically feasible. The net present cost (NPC) and cost of energy (COE) of the system are obtained at $230,223 and $0.0409, respectively. The results indicate that the TLC plays a key role in the optimal operation of the PV system and the reduction in greenhouse gas emission productions.

scholarly journals DC-Microgrid Operation Planning for an Electric Vehicle Supply Infrastructure

2019 ◽  
Vol 9 (13) ◽  
pp. 2687 ◽  
Author(s):  
Benedetto Aluisio ◽  
Sergio Bruno ◽  
Luca De Bellis ◽  
Maria Dicorato ◽  
Giuseppe Forte ◽  
...  
Keyword(s):  
Power Systems ◽  
Electric Vehicle ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Operation Planning ◽  
Energy Storage Devices ◽  
Dc Microgrid ◽  
Charging Station ◽  
Smart Charging ◽  
Non Linear

The integration of electric vehicles (EVs) in power systems can be encouraged by charging station diffusion. These stations can perform smart charging processes, and can take advantage of the involvement of distributed generation sources in a microgrid framework. Furthermore, since photovoltaic batteries and EVs are sources based on direct current (DC), the realization of a DC microgrid structure is promising, though challenging. In this paper, a mixed-integer linear procedure for determining optimal operation planning of a DC-based electric vehicle supply infrastructure is proposed. The procedure aims at optimizing daily operational costs, based on forecast of photovoltaic production and EV exploitation. Peculiar aspects of energy storage devices and of the DC microgrid framework are accounted for through a non-linear iterative procedure. The proposed approach is applied to a test DC microgrid on different operation days and its effectiveness is compared to non-linear formulation solved by means of a genetic algorithm.

scholarly journals Secure Operation of Integrated Natural Gas and Electricity Transmission Networks

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4954
Author(s):  
Ali Mohammad Rostami ◽  
Hossein Ameli ◽  
Mohammad Taghi Ameli ◽  
Goran Strbac
Keyword(s):  
Natural Gas ◽  
Energy System ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Contingency Analysis ◽  
Transmission Networks ◽  
Operational Model ◽  
Electricity Transmission ◽  
Integer Nonlinear Programming

The interaction between natural gas and electricity networks is becoming more significant due to the projected large penetration of renewables into the energy system to meet the emission targets. This is due to the role of gas-fired plants in providing backup to renewables as the linkage between these networks. Therefore, this paper proposes a deterministic coordinated model for the secure and optimal operation of integrated natural gas and electricity transmission networks by taking into account the N-1 contingency analysis on both networks. In order to reduce the computational burden and time, an iterative algorithm is proposed to select the critical cases and neglect other contingencies, which do not have a significant impact on the energy system. The proposed integrated mixed-integer nonlinear programming operational model is evaluated and compared to another enhanced separated model on the IEEE 24-bus and 15-node gas test systems. The results emphasize the importance and effectiveness of the proposed framework (up to 6.7% operational costs savings are achieved).

scholarly journals Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1124 ◽  
Author(s):  
Byeong-Cheol Jeong ◽  
Dong-Hwan Shin ◽  
Jae-Beom Im ◽  
Jae-Young Park ◽  
Young-Jin Kim
Keyword(s):  
Energy Storage ◽  
Scheduling Algorithm ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Operating Cost ◽  
Optimal Schedule ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Actual Case

Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm where ESS power inputs are optimally determined to minimize the microgrid (MG) operation cost. The proposed algorithm consists of two stages. In the first stage, hourly schedules during a day are optimized one day in advance with the objective of minimizing the operating cost. In the second stage, the optimal schedule obtained from the first stage is repeatedly updated every 5 min during the day of operation to compensate for the uncertainties in load demand and RES output power. The ESS model is developed considering operating efficiencies and then incorporated in mixed integer linear programming (MILP). Penalty functions are also considered to acquire feasible optimal solutions even under large forecasting errors in RES generation and load variation. The proposed algorithm is verified in a campus MG, implemented using ESSs and photovoltaic (PV) arrays. The field test results are obtained using open-source software and then compared with those acquired using commercial software.

scholarly journals Heuristic Storage System Sizing for Optimal Operation of Electric Vehicles Powered by Photovoltaic Charging Station

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Erik Blasius ◽  
Erik Federau ◽  
Przemyslaw Janik ◽  
Zbigniew Leonowicz
Keyword(s):  
Electric Vehicles ◽  
Smart Cities ◽  
Storage System ◽  
Measurement Data ◽  
Optimal Operation ◽  
Storage Device ◽  
Distribution Grid ◽  
Pv System ◽  
Charging Station ◽  
Charging Demand

This paper discusses the utilisation of PV systems for electric vehicles charging for transportation requirements of smart cities. The gap between PV power output and vehicles charging demand is highly variable. Therefore, there is a need for additional support from a public distribution grid or a storage device in order to handle the residual power. Long term measurement data retrieved from a charging station for 15 vehicles equipped with a PV system were used in the research. Low and high irradiation seasons influenced the PV output. The charging demand of electric vehicles varied over the course of a year and was correlated to weather conditions. Therefore, the sizing and performance of a supportive storage device should be evaluated in a statistical manner using long period observations.

scholarly journals Calculation and Analysis of the Interval Power Flow for Distributed Energy System Based on Affine Algorithm

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 600
Author(s):  
Bin Ouyang ◽  
Lu Qu ◽  
Qiyang Liu ◽  
Baoye Tian ◽  
Zhichang Yuan ◽  
...  
Keyword(s):  
Power Flow ◽  
Load Condition ◽  
Energy Systems ◽  
Energy System ◽  
Optimal Operation ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Distributed Energy ◽  
Low Load ◽  
Distributed Energy System

Due to the coupling of different energy systems, optimization of different energy complementarities, and the realization of the highest overall energy utilization rate and environmental friendliness of the energy system, distributed energy system has become an important way to build a clean and low-carbon energy system. However, the complex topological structure of the system and too many coupling devices bring more uncertain factors to the system which the calculation of the interval power flow of distributed energy system becomes the key problem to be solved urgently. Affine power flow calculation is considered as an important solution to solve uncertain steady power flow problems. In this paper, the distributed energy system coupled with cold, heat, and electricity is taken as the research object, the influence of different uncertain factors such as photovoltaic and wind power output is comprehensively considered, and affine algorithm is adopted to calculate the system power flow of the distributed energy system under high and low load conditions. The results show that the system has larger operating space, more stable bus voltage and more flexible pipeline flow under low load condition than under high load condition. The calculation results of the interval power flow of distributed energy systems can provide theoretical basis and data support for the stability analysis and optimal operation of distributed energy systems.

scholarly journals Interdependencies of Infrastructure Investment Decisions in Multi-Energy Systems—A Sensitivity Analysis for Urban Residential Areas

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 112-145
Author(s):  
Daniel Then ◽  
Johannes Bauer ◽  
Tanja Kneiske ◽  
Martin Braun
Keyword(s):  
Sensitivity Analysis ◽  
Energy Demand ◽  
Business Models ◽  
Linear Optimization ◽  
Investment Decisions ◽  
Energy System ◽  
Mixed Integer ◽  
The European Union ◽  
Residential Areas ◽  
Mixed Integer Linear Optimization

Considering the European Union (EU) climate targets, the heating sector should be decarbonized by 80 to 95% up to 2050. Thus, the macro-trends forecast increasing energy efficiency and focus on the use of renewable gas or the electrification of heat generation. This has implications for the business models of urban electricity and in particular natural gas distribution network operators (DNOs): When the energy demand decreases, a disproportionately long grid is operated, which can cause a rise of grid charges and thus the gas price. This creates a situation in which a self-reinforcing feedback loop starts, which increases the risk of gas grid defection. We present a mixed integer linear optimization model to analyze the interdependencies between the electricity and gas DNOs’ and the building owners’ investment decisions during the transformation path. The results of the investigation in a real grid area are used to validate the simulation setup of a sensitivity analysis of 27 types of building collectives and five grid topologies, which provides a systematic insight into the interrelated system. Therefore, it is possible to identify building and grid configurations that increase the risk of a complete gas grid shutdown and those that should be operated as a flexibility option in a future renewable energy system.

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