Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support

López, María Dolores Ruiz; Torre, S. de la; Martín, Sebastián; Aguado, José A.

doi:10.1016/j.ijepes.2014.07.065

Cited by 191 publications

(102 citation statements)

References 33 publications

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“…Step 1: Define the "Model bank" of the power system area i , with LFC (Thermal power plant) and EV, as in (3) and (4). The study system is linearized around normal operating point, so that the , Step 4: 1 k k = + , return to step 2.…”

Section: Detail Strategy Of the Mimo-mpc For Lfc And Soc Controlmentioning

confidence: 99%

“…Effective integrations of EVs into the grid are able to improve the power system ancillary services such as demand-side management [4], distribution network planning [5][6], wide area stability enhancement [7], and alleviation of frequency fluctuation [2,[8][9][10][11][12][13][14]. The power charging control of EVs in order to alleviate the frequency fluctuation has been proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

Pahasa¹,

Ngamroo²

2017

Journal of Electrical Engineering and Technology

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-This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective modelbased prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

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Section: Detail Strategy Of the Mimo-mpc For Lfc And Soc Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

Pahasa¹,

Ngamroo²

2017

Journal of Electrical Engineering and Technology

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“…In [16], the authors defined agents responsible for load, generation and storage management, and proposed an agent profit maximization model, which was applied to an electric vehicle management system based on IEEE 37-bus distribution grid. In [17] an architecture for provisioning of demand response services from aggregated small residential consumers was presented, along with its MAS implementation.…”

Section: Resource Modeling and Distributed Decision Making In Smart Gridmentioning

confidence: 99%

An Event-Based Resource Management Framework for Distributed Decision-Making in Decentralized Virtual Power Plants

2016

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Abstract:The Smart Grid incorporates advanced information and communication technologies (ICTs) in power systems, and is characterized by high penetration of distributed energy resources (DERs). Whether it is the nation-wide power grid or a single residential building, the energy management involves different types of resources that often depend on and influence each other. The concept of virtual power plant (VPP) has been proposed to represent the aggregation of energy resources in the electricity market, and distributed decision-making (DDM) plays a vital role in VPP due to its complex nature. This paper proposes a framework for managing different resource types of relevance to energy management for decentralized VPP. The framework views VPP as a hierarchical structure and abstracts energy consumption/generation as contractual resources, i.e., contractual offerings to curtail load/supply energy, from third party VPP participants for DDM. The proposed resource models, event-based approach to decision making, multi-agent system and ontology implementation of the framework are presented in detail. The effectiveness of the proposed framework is then demonstrated through an application to a simulated campus VPP with real building energy data.

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“…Many authors consider implementing detailed state-space or time-series models of flexible load directly within system dispatch algorithms [6], [7]. This approach provides an assessment of the theoretical value that DR would provide if its flexibility were fully accessible, but the complexity of this representation of DR renders it impractical.…”

Section: Introductionmentioning

confidence: 99%

On the Inclusion of Energy-Shifting Demand Response in Production Cost Models: Methodology and a Case Study

O׳Connell¹,

Hale

Doebber

et al. 2015

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In the context of future power system requirements for additional flexibility, demand response (DR) is an attractive potential resource. Its proponents widely laud its prospective benefits, which include enabling higher penetrations of variable renewable generation at lower cost than alternative storage technologies, and improving economic efficiency. In practice, DR from the commercial and residential sectors is largely an emerging, not a mature, resource, and its actual costs and benefits need to be studied to determine promising combinations of physical DR resource, enabling controls and communications, power system characteristics, regulatory environments, market structures, and business models. The work described in this report focuses on the enablement of such analysis from the production cost modeling perspective. In particular, we contribute a bottom-up methodology for modeling load-shifting DR in production cost models. The resulting model is sufficiently detailed to reflect the physical characteristics and constraints of the underlying flexible load, and includes the possibility of capturing diurnal and seasonal variations in the resource. Nonetheless, the model is of low complexity and thus suitable for inclusion in conventional unit commitment and market clearing algorithms. The ability to simulate DR as an operational resource on a power system over a year facilitates an assessment of its time-varying value to the power system.The modeling methodology is demonstrated through a case study of aggregated supermarket refrigeration systems providing balancing energy reserves in real-time markets at different levels of variable generation (VG). This DR resource is implemented in a test power system that represents a subset of the U.S Western Interconnection centered on Colorado. The value of DR from the population of supermarkets in Colorado is found to be $32.85 per kilowatt-year (kW-yr) presuming no other DR resources. The value decreases significantly (to $6.95/kW-year in the most extreme case) when we increase the capacity of the DR resource to naïvely represent the incorporation of DR from other flexible loads (in actuality, other DR resources will have different characteristics, such that the decrease in value will not be as steep). Refrigeration DR is found to offer greater value to the power system during the winter months than the summer months due to operational constraints that limit the flexibility of the resource during the summer. The value of DR is found to increase as the penetration of VG increases, reaching $46.05/kWyear for our baseline DR penetration and a variable generation (VG) penetration of 55%. We do see a plateau in the value of DR going from 45% to 55% VG. This is attributable to the inability of DR to provide energy storage on horizons longer than 24 hours.Overall, this work is a study in methodology. The case study is included primarily to show that the model is working properly and that this line of research is worthwhile. The reported numbers do not represent a true value o...

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Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support

Cited by 191 publications

References 33 publications

Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

An Event-Based Resource Management Framework for Distributed Decision-Making in Decentralized Virtual Power Plants

On the Inclusion of Energy-Shifting Demand Response in Production Cost Models: Methodology and a Case Study

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