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231 results on '"Amraee, Turaj"'

52. Coordinated multi-objective scheduling of a multi-energy virtual power plant considering storages and demand response

Author

Olanlari, Farzin Ghasemi (author), Amraee, Turaj (author), Moradi Sepahvand, M. (author), Ahmadian, Ali (author), Olanlari, Farzin Ghasemi (author), Amraee, Turaj (author), Moradi Sepahvand, M. (author), and Ahmadian, Ali (author)

Abstract

A virtual power plant (VPP) is a solution that brings distributed generation (DG) resources together and allows them to be optimally utilized to meet load demands in the presence of technical and pollution constraints. Electricity, heat, and natural gas are interdependent at the levels of generation, transmission, and consumption, and the interactions of these energy sources need to be considered. This paper presents an optimal model for daily operation of a multi-energy virtual power plant (MEVPP), including electric, thermal, and natural gas sectors. MEVPP includes small-scale gas-fired and non-gas-fired DGs, combined heat and power (CHP), power to gas (P2G), boilers, electrical storage, electric vehicles (EV), and thermal storage. Renewable energy resources (RES), including wind turbines (WT), photovoltaic (PV), and PV-thermal (PVT), also supply P2G technology. Smart grid technologies such as price-based demand response (PBDR) and incentive-based demand response (IBDR) are employed for electric loads. The proposed MEVPP model is eligible to participate in day-ahead electricity, natural gas, heat markets, and electrical spinning reserve market. The scheduling model is multi-objective to maximize MEVPP profit and minimize carbon dioxide emissions. The Epsilon constraint method is utilized to solve the problem, and the best Pareto point is chosen using the fuzzy satisfying approach., Intelligent Electrical Power Grids

Published
2022
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53. Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Author

Moradi Sepahvand, M. (author), Amraee, Turaj (author), Aminifar, Farrokh (author), Akbari, Amirhossein (author), Moradi Sepahvand, M. (author), Amraee, Turaj (author), Aminifar, Farrokh (author), and Akbari, Amirhossein (author)

Abstract

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model., Intelligent Electrical Power Grids

Published
2022
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59. Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings.

Author

Zareein, Mohammad, Sahebkar Farkhani, Jalal, Nikoofard, Amirhossein, and Amraee, Turaj

Subjects
MICROGRIDS, ENERGY management, INTELLIGENT buildings, COMMERCIAL buildings, GAME theory, COMMUNITIES, PRICES
Abstract

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid. [ABSTRACT FROM AUTHOR]

Published
2023
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67. Robust fuzzy model predictive control for voltage regulation in islanded microgrids.

Author

Mottaghizadeh, Masood, Aminifar, Farrokh, Amraee, Turaj, and Sanaye‐Pasand, Majid

Subjects
MICROGRIDS, PREDICTIVE control systems, VOLTAGE regulators, FUZZY logic, LINEAR matrix inequalities
Abstract

An effective robust fuzzy model predictive control (RFMPC) method for secondary voltage control in islanded microgrids (μGs) is presented here. In contrast to the existing techniques, which require a detailed model of μG and an ideal communication network between μG central controller and primary local controllers, RFMPC is synthesized for a non‐linear model of the μG with various time delays, uncertainties, and bounded disturbances. The famous Takagi‐Sugeno fuzzy approach is adopted to approximate the inherently non‐linear model of μG by locally linear dynamics. The Lyapunov–Razumikhin functional method is exploited to deal with time delays. In this regard, sufficient conditions are provided in the form of linear matrix inequalities (LMIs). Then, a sequence of control laws corresponding to a set of terminal constraints is computed offline. Doing so, the online stage is reduced to solving a convex problem with LMI constraints considering the sequence of constraint sets obtained in the offline stage, thereby reducing the computational burden significantly. Robust positive invariance and input‐to‐state stability property concerning communication network deficiency are then speculated. The effectiveness of the proposed RFMPC is verified via a comprehensive suite of simulations in the MATLAB/SimPowerSystems environment. [ABSTRACT FROM AUTHOR]

Published
2022
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