Robust Short-Term Scheduling of Smart Distribution Systems Considering Renewable Sources and Demand Response Programs

Ghahramani, Mehrdad; Nazari-Heris, Morteza; Zare, Kazem; Mohammadi‐Ivatloo, Behnam

doi:10.1007/978-3-030-04296-7_14

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…In this model, no attention has been paid to electric vehicles and supplying thermal loads. In [ 14 ] , probabilistic microgrid planning with the presence of wind power plant and responsive load management is proposed. Charging and discharging management of electric vehicles is not done in this article.…”

Section: Inmentioning

confidence: 99%

Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles

2023

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The global electricity network and natural gas have been developed over a broad geographical area worldwide. So far, various investigations have been performed in relation to energy infrastructures, however the combination of these systems with each other, which has many advantages, has not examined much. For this reason, electric and thermal loads have been interconnected by CHP units, and the combined study of power and heat has helped the operator to propose a more optimal structure for energy management. Therefore, integrating different energy carriers with each other and using the benefits of each one can increase the efficiency and reliability along with optimizing system performance. Such a capability in structure and operation requires a comprehensive framework to describe the effects of combining different energy carriers on the economic and environmental indicators of energy systems. In this paper, the microgrid has been modeled as an energy hub in order to combine different energy carriers and perform conversions in them to provide the optimal electric and thermal load required by the consumer. This structure has been created by managing the incoming carriers of gas, electricity and renewable energy and managing their conversion and storage in order to minimize the cost of energy supply and environmental pollutants. In order to improve the performance of the microgrid and help the operator of the microgrid, in addition to managing generation units, a responsive load management strategy and charging and discharging of electric vehicles have also been proposed. Epsilon constraints and fuzzy decision-making methods have been used to solve this problem. The results indicate that in the optimal state with the application of responsive loads, the total cost of operation is reduced by more than 6% and the cost of environmental pollutants is reduced by about 13%.

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Section: Inmentioning

confidence: 99%

Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles

2023

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“…So far, several articles have been published about the optimal operation of micro-grids and their challenges. In [6], the role of demand response in thermal-wind production programming is considered using probabilistic programming. In this reference, researchers have dealt with the modeling of responsive loads in two modes of response based on encouragement and accountability based on pricing and have provided models for them.…”

Section: Introductionmentioning

confidence: 99%

Random Modeling of Optimal Economic, Security and Environmental Operation of Micro-grid by Managing Responsive Loads and Charging and Discharging Electric Vehicles

Bakhshinejad

Tavakoli

Moghaddam

2021

mjee

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The micro-grid operator must provide the energy required by its customers at the lowest cost and consider issues such as greenhouse gas emissions and security. The operator is faced with a multi-objective optimization problem in which customer demand must be provided at the lowest cost and safely. This research provides a new energy management system for islanded micro-grids. The small size of the islanded micro-grids, the high level of intermittent operation and the low inertia of distributed generation of inverter energy production resources make the frequency and voltage security two vital factors in the energy management system, which must be managed alongside economic-environmental policies. In this study, two practical tools are provided to help with the optimal operation and increase the profitability of the micro-grid operator. The first tool is the optimal and managed use of the V2G mode of electric vehicles. In the proposed approach, not only the penetration of electric vehicles in the network is managed but also this equipment is used to solve some of the network's challenges. The second tool is responsive loads and demand response programs in order to achieve the goals of the micro-grid operator. Covering the uncertainty of renewable energy sources by responsive loads, and how to model a demand response program in a micro-grid, are followed in this study. The strategy pursued several goals, including reducing energy and load costs, reducing the cost of charging EVs, and improving network parameters and security, such as voltage and frequency. The results confirm the effectiveness of the proposed approach.

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“…In Reference 16, multi‐objective optimization is implemented for determining operation and emission costs, considering the uncertainty of wind speed. In Reference 17, researchers firstly modeled the responsive loads in the two modes of response according to their encouragement and responsiveness based on pricing and have provided models for them. However, the state of energy exchange with the upstream grid is not discussed in the objective functions.…”

Section: Introductionmentioning

confidence: 99%

Security‐constrained optimal scheduling and operation of island microgrids considering demand response and electric vehicles

Divani

Najafi

Ghaedi

et al. 2021

Int Trans Electr Energ Syst

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As an independent unit, microgrid operator (MGO) should respond to the energy demand of customers with an optimal provision cost. Apart from the ability to guarantee the security of microgrid (MG) operation, the MGO should consider the greenhouse emission effects as well. This paper investigates a novel energy management system for an islanded MG. In the proposed strategy, two efficient methodologies are used to maximize the profit and security of the MG: (a) the optimal operation of electric vehicles (EVs) in vehicle-togrid (V2G) mode and (b) the use of demand response (DR) program. Besides, a hierarchical control structure is proposed to manage frequency and voltage uncertainties in a permissible range. Improving the reliability of the MG is another goal of the proposed strategy, which is pursued by the studied control-List of Symbols and Abbreviations: l, t, s, indices of group of loads, time period, and scenario, respectively; j, i, k, indices of WT, PV, and DG units, respectively; n, m, indices of buses; B, L, set of buses and loads; E, index of electric vehicle; N L ,N T ,N s , number of scenarios, time period, and group of loads; N J , N I ,N k , number of WT, PV, and DG units; F, objective function; P j,t , scheduled DG active power (kW); P k,t , scheduled WT active power (kW); P i,t , scheduled PV active power (kW); Q j,t , scheduled DG reactive power (kW); Q k,t , scheduled WT reactive power (kW); Q i,t , scheduled PV reactive power (kW); P ref j,t,s , DG power generation reference point for regulation (kW); P l,t , active load demand (kWh); Q l,t , reactive load demand (kWh); P L_V2G e,t , P L_G2V e,t , power loss for V2G (discharging) and G2V (charging) modes (kW); P LE e,t , inverter power loss (kW); P V2G e,t , P G2V e,t , exchanges power between EV and grid; P shed l,t,s , Q shed l,t,s , emergency active and reactive power outage, respectively (KW/KVAR); P min j , P max j , minimum and maximum DG generation power (kW); R U:max j:t , R D:max j:t, maximum participation rate of unit j in providing incremental/decrement spinning reserved power (kW); R NS:max j:t , maximum participation rate of unit j in providing non-spinning reserved power (kW); P max n,m , maximum power passing through the connecting line between bus n and m; C max e , capacity of the battery (kW); V n:t , V m,t , voltage of nodes n and m; ΔV max , maximum voltage deviation for bus n; δ n:t , δ m:t , voltage angle of nodes n and m; G n:r , B n,r , conductance and suspension of the lines between nodes n and m; SOC EV e,tÀ1 , initial level of EV SOC; X EV e,t , binary variable, charging/discharging state; η BTB e , inverter efficiency; B EV e,t , exchanged power between EV and inverter; R NS j,t , scheduled nonspinning reserved power (kW); R U j,t , scheduled incremental spinning reserved power (kW); R D j,t , scheduled decremental spinning reserved power (kW); r NS j,t,s , realized non-spinning reserved power (kW); r U j,t,s , realized incremental spinning reserved power (kW); r D j,t,s , realized decremental spinning reserved power (...

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Robust Short-Term Scheduling of Smart Distribution Systems Considering Renewable Sources and Demand Response Programs

Cited by 8 publications

References 27 publications

Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles

Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles

Random Modeling of Optimal Economic, Security and Environmental Operation of Micro-grid by Managing Responsive Loads and Charging and Discharging Electric Vehicles

Security‐constrained optimal scheduling and operation of island microgrids considering demand response and electric vehicles

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