Smart distribution system operations with price-responsive and controllable loads

Sharma, Isha; Bhattacharya, Kankar; Cañizares, Claudio A.

doi:10.1109/pesgm.2015.7286405

Cited by 14 publications

(34 citation statements)

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“…• Fuel cost model of thermal generators Fuel cost of a thermal generator can be described as a quadratic function in Equation (5), or a quadratic function with valve-point effects in Equation (6). 14,16,43 d i and e i are coefficients describing the valve-point loading effect.…”

Section: Formulation Of Opfmentioning

confidence: 99%

“…The technical literature on the subject proposes a methodology to calculate the cost of underestimating or overestimating the available power of the stochastic generator as an additional term in the objective function of the OPF. 6,7,45 The concept of underestimation (C u,i ) and overestimation (C o,i ) costs is due to the fact that there may be a difference between the available generated power (W av,i ) and that was scheduled (W S,i ). In this way, when scheduled power is less than the available power, there is an underestimation cost, since not all available power will be used.…”

Section: Control Models For Controllable Pv Energy Systemsmentioning

confidence: 99%

“…These sources are generally considered noncontrollable in economical dispatch and optimal power flow (OPF) problems of power systems. However, when a system has a significant penetration of these sources, it is necessary to have tools to decide how much power to dispatch from these sources (ie, make them controllable), and such tools should consider the probability density function of their respective primary sources, as shown in Ziadi et al and Sharma et al…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman

Rivera

et al. 2019

Int Trans Electr Energ Syst

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Summary The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back‐up unit (storage). Uncertain solar irradiance and wind speed are simulated via log‐normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed‐rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.

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Section: Formulation Of Opfmentioning

confidence: 99%

Section: Control Models For Controllable Pv Energy Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman

Rivera

et al. 2019

Int Trans Electr Energ Syst

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“…It can be proven that a quadratic utility function leads to a consumption P flex k,i that is linearly dependent on the electricity price [31]. Typically, the available flexibility depends on the time of the day.…”

Section: System Modelmentioning

confidence: 99%

Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

2015

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In this paper, a locational marginal pricing algorithm is proposed to control the voltage in unbalanced distribution grids. The increasing amount of photovoltaic (PV) generation installed in the grid may cause the voltage to rise to unacceptable levels during periods of low consumption. With locational prices, the distribution system operator can steer the reactive power consumption and active power curtailment of PV panels to guarantee a safe network operation. Flexible loads also respond to these prices. A distributed gradient algorithm automatically defines the locational prices that avoid voltage problems. Using these locational prices results in a minimum cost for the distribution operator to control the voltage. Locational prices can differ between the three phases in unbalanced grids. This is caused by a higher consumption or production in one of the phases compared to the other phases and provides the opportunity for arbitrage, where power is transferred from a phase with a low price to a phase with a high price. The effect of arbitrage is analyzed. The proposed algorithm is applied to an existing three-phase four-wire radial grid. Several simulations with realistic data are performed.

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“…Demand side management (DSM), a key component of future smart grids, can increase the efficiency of electric grids by modifying consumer's electric demand through various methods such as financial incentives and behavioral change [1]- [4]. DSM focuses on interacting between utility and customers.…”

Section: Introductionmentioning

confidence: 99%

Demand Side Management for Homes in Smart Grids

Narimani

2019

2019 North American Power Symposium (NAPS)

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Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times, within which their consumption can be shifted from the normal schedules. Electric vehicles (EVs), with vehicle-to-grid (V2G) and vehicle-to-home (V2H) abilities, Energy storage systems (ESS), and Renewable Energy Resource as a distributed generation are considered at the end-user premises to improve the energy management efficiency. A mixed-integer linear programming (MILP) framework is implemented to solve the proposed problem. Numerical results show that the proposed approach can reduce customers' electricity bill without sacrificing their comfort and fully exploit EVs and ESS capacities.Index Terms--demand side management, renewable energy sources, electric vehicles, flexible loads, smart grid.

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Smart distribution system operations with price-responsive and controllable loads

Cited by 14 publications

References 0 publications

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

Demand Side Management for Homes in Smart Grids

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