scholarly journals An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 129
Author(s):  
Ricardo Faia ◽  
João Soares ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Electric Vehicles ◽  
Electricity Market ◽  
Optimization Model ◽  
Local Community ◽  
Programming Model ◽  
Mixed Integer ◽  
Energy Costs ◽  
Local Energy ◽  
Distribution Grid ◽  
Energy Community

Electric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs.

scholarly journals Local Electricity Markets for Electric Vehicles: An Application Study Using a Decentralized Iterative Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Ricardo Faia ◽  
João Soares ◽  
Mohammad Ali Fotouhi Ghazvini ◽  
John F. Franco ◽  
Zita Vale
Keyword(s):  
Electric Vehicles ◽  
Electricity Markets ◽  
Execution Time ◽  
Optimal Solution ◽  
Peer To Peer ◽  
Market Model ◽  
Mixed Integer ◽  
Local Energy ◽  
Low Carbon ◽  
Energy Community

Local electricity markets are emerging solutions to enable local energy trade for the end users and provide grid support services when required. Various models of local electricity markets (LEMs) have been proposed in the literature. The peer-to-peer market model appears as a promising structure among the proposed models. The peer-to-peer market structure enables electricity transactions between the players in a local energy system at a lower cost. It promotes the production from the small low–carbon generation technologies. Energy communities can be the ideal place to implement local electricity markets as they are designed to allow for larger growth of renewable energy and electric vehicles, while benefiting from local transactions. In this context, a LEM model is proposed considering an energy community with high penetration of electric vehicles in which prosumer-to-vehicle (P2V) transactions are possible. Each member of the energy community can buy electricity from the retailer or other members and sell electricity. The problem is modeled as a mixed-integer linear programing (MILP) formulation and solved within a decentralized and iterative process. The decentralized implementation provides acceptable solutions with a reasonable execution time, while the centralized implementation usually gives an optimal solution at the expense of reduced scalability. Preliminary results indicate that there are advantages for EVs as participants of the LEM, and the proposed implementation ensures an optimal solution in an acceptable execution time. Moreover, P2V transactions benefit the local distribution grid and the energy community.

scholarly journals How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4163
Author(s):  
Giuseppe Barone ◽  
Giovanni Brusco ◽  
Daniele Menniti ◽  
Anna Pinnarelli ◽  
Gaetano Polizzi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Experimental Test ◽  
Local Community ◽  
Transition Process ◽  
Local Energy ◽  
Smart Metering ◽  
Smart Meters ◽  
Smart Charging ◽  
Energy Community

The 2018/2001/EU renewable energy directive (RED II) underlined the strategic role of energy communities in the EU transition process towards sustainable and renewable energy. In line with the path traced by RED II, this paper proposes a solution that may help local energy communities in increasing self-consumption. The proposed solution is based on the combination of smart metering and smart charging. A set of smart meters returns the profile of each member of the community with a time resolution of 5 s; the aggregator calculates the community profile and regulates the charging of electric vehicles accordingly. An experimental test is performed on a local community composed of four users, where the first is a consumer with a Nissan Leaf, whereas the remaining three users are prosumers with a photovoltaic generator mounted on the roof of their home. The results of the experimental test show the feasibility of the proposed solution and demonstrate its effectiveness in increasing self-consumption. The paper also calculates the subsidy that the community under investigation would receive if the current Italian incentive policies for renewables were extended to local energy communities; this subsidy is discussed in comparison with the subsidies that the three prosumers individually receive thanks to the net metering mechanism. This paper ends with an economic analysis and calculation of savings on bills when the four users create the local energy community and adopt the proposed combination of smart metering and smart charging.

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 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 The Electric Vehicle Time-of-Use Price Optimization Model Considering the Demand Response

2018 ◽  
Vol 160 ◽  
pp. 02009
Author(s):  
Zejing Shi ◽  
Ninghui Zhu ◽  
Jinsong Yu
Keyword(s):  
Electric Vehicles ◽  
Optimization Model ◽  
Prediction Models ◽  
Distribution Grid ◽  
Power Prediction ◽  
Peak Shaving ◽  
Satisfaction Index ◽  
The Monte Carlo Method ◽  
Load Peak ◽  
Coordinated Charging

A large number of electric vehicles connecting to the distribution grids usually introduce significant fluctuations to the grid and the loads. To solve the problem, guiding the users coordinated charging is proposed. Firstly, the uncontrolled charging power prediction models of electric vehicles are established, and the Monte Carlo method is adopted to simulate the power demands of different electric vehicles, and the influences on the load peak-valley ratios and the voltages and losses of the grid are all analyzed. Then the vehicle responses model considering the time-of-use price is analyzed, and the vehicle response ratios are obtained under different time-of-use prices. Finally the multi-objective optimization model is constructed including the minimum peak-valley ratio, maximum consumption satisfaction index and cost satisfaction index. In the procedure, vehicles and the grid are both taken into account. The results indicate the proposed method could guide the users coordinated charging, and the peak shaving and valley filling is also achieved, and the operation of the distribution grid is improved.

scholarly journals Integration of Electric Vehicles in Low-Voltage Distribution Networks Considering Voltage Management

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4125
Author(s):  
Miguel Carrión ◽  
Rafael Zárate-Miñano ◽  
Ruth Domínguez
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Low Voltage ◽  
Distribution Network ◽  
Planning Horizon ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Distribution Grid

The expected growth of the number of electric vehicles can be challenging for planning and operating power systems. In this sense, distribution networks are considered the Achilles’ heel of the process of adapting current power systems for a high presence of electric vehicles. This paper aims at deciding the maximum number of three-phase high-power charging points that can be installed in a low-voltage residential distribution grid. In order to increase the number of installed charging points, a mixed-integer formulation is proposed to model the provision of decentralized voltage support by electric vehicle chargers. This formulation is afterwards integrated into a modified AC optimal power flow formulation to characterize the steady-state operation of the distribution network during a given planning horizon. The performance of the proposed formulations have been tested in a case study based on the distribution network of La Graciosa island in Spain.

scholarly journals Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2249 ◽  
Author(s):  
Emanuel Canelas ◽  
Tânia Pinto-Varela ◽  
Bartosz Sawik
Keyword(s):  
Portfolio Optimization ◽  
Electricity Market ◽  
Value At Risk ◽  
Programming Model ◽  
Spot Market ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Expected Cost ◽  
Seasonal Behavior

Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and the conditional value-at-risk (CVaR) minimization are used as objective function. Three case studies illustrate the model applicability through the characterization of how the portfolio evolves with different demand profiles and how to take advantage from seasonality characteristic in the spot market. A scenario analysis is explored to reflect the uncertainty on the price of the spot market. The expected cost and CVaR are optimized for each case study and the portfolio analysis for each risk posture is characterized. The results illustrate the advantage to reduce costs and risk if the prices seasonality is considered, triggering to an adaptive seasonal behavior, which support the decision-maker decision towards its goals.

scholarly journals Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 777 ◽  
Author(s):  
Ping Che ◽  
Yanyan Zhang ◽  
Jin Lang
Keyword(s):  
Electricity Market ◽  
Emission Intensity ◽  
Tax Policy ◽  
Programming Model ◽  
Carbon Tax ◽  
Electric Power Industry ◽  
Decomposition Algorithm ◽  
Mixed Integer ◽  
Tax Rate ◽  
The Impact

We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.

Regional Power Planning Optimization Model and Scenario Analysis Considering the Environmental Cost

2013 ◽  
Vol 805-806 ◽  
pp. 1122-1128
Author(s):  
Zong Wu Wang ◽  
Guo He Huang ◽  
Xiao Kun Li
Keyword(s):  
Linear Programming ◽  
Scenario Analysis ◽  
Pollution Control ◽  
Optimization Model ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Regional Power ◽  
Planning Optimization ◽  
Decision Alternatives

In this study, a regional power planning optimization model (RPPOM) is developed considering the environmental cost and the restriction of resource and environment, based on interval linear programming and mixed integer linear programming. Model is applied to a case study on the power planning in Henan province, and scenario analysis is conducted. Interval solutions associated with scenario of pollution control have been obtained. They can be used for generating decision alternatives and helping decision makers identify desired power policies for power planning to meet the growth in electricity demand considering the constraints of resources and environment with a minimized system cost. Scenario analysis of environmental pollution control at different levels can also be tackled.

Train Scheduling Method to Reduce Substation Energy Consumption and Peak Power of Metro Transit Systems

2021 ◽  
pp. 036119812097467
Author(s):  
Bo Jin ◽  
Xiaoyun Feng ◽  
Qingyuan Wang ◽  
Pengfei Sun ◽  
Qian Fang
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Peak Power ◽  
Programming Model ◽  
Rapid Development ◽  
Mixed Integer ◽  
Power Requirement ◽  
Transit Systems ◽  
Train Scheduling ◽  
Timetable Optimization

The rapid development of metro transit systems brings very significant energy consumption, and the high service frequency of metro trains increases the peak power requirement, which affects the operation of systems. Train scheduling optimization is an effective method to reduce energy consumption and substation peak power by adjusting timetable parameters. This paper proposes a train timetable optimization model to coordinate the operation of trains. The overlap time between accelerating and braking phases is maximized to improve the utilization of regenerative braking energy (RBE). Meanwhile, the overlap time between accelerating phases is minimized to reduce the substation peak power. In addition, the timetable optimization model is rebuilt into a mixed integer linear programming model by introducing logical and auxiliary variables, which can be solved by related solvers effectively. Case studies based on one of Guangzhou Metro Lines indicate that, for all-day operation, the utilization of RBE would likely be improved on the order of 23%, the substation energy consumption would likely be reduced on the order of 14%, and the duration of substation peak power would likely be reduced on the order of 66%.

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