Scenario based uncertainty modeling of electricity market prices

Sharma, Kailash Chand; Bhakar, Rohit; Tiwari, Harpal; Chawda, Sandeep

doi:10.1109/cera.2017.8343320

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Due to higher order terms and necessity to estimate large number of parameters, both auto‐regressive and moving average models are unsuitable for modelling any stochastic process individually. But auto‐regressive‐moving‐average (ARMA) model can accurately model any stochastic process with minimum parameters 32 . A typical ARMA express as:

ρ_{i}^{buy} = \sum_{i = 1}^{N} ((), {OF}_{SH}^{EMS}) ∙ ρ_{(i - 1)}^{buy} + ε_{i} - \sum_{i = 1}^{N} E_{i}^{Market} ε_{(i - 1)},

where the

ρ_{i}^{buy}

is the energy price,

{OF}_{SH}^{EMS}

is the total cost of SH,

E_{i}^{Market}

is the purchase energy of SH, in the interval of i th.…”

Section: Numerical Studies and Simulation Resultsmentioning

confidence: 99%

Arisk‐basedenergy management system design for grid‐connectedsmart homes

Mohammadi

Faghihi

Kazemi

et al. 2021

Int Trans Electr Energ Syst

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Summary One of the energy management system (EMS) goals in the smart home (SH) is to achieve cost reduction besides consumer risk minimization. For these purposes, SH will be equipped with combined heat and power (CHP) generation, wind turbine (WT), and photovoltaic (PV) resources. EMS in SH faces uncertainty due to variable generation of these resources and in‐operation of the switch connected to the network. In this article, proposed comprehensive algorithm for EMS of SH including WT, PV, battery energy storage system, CHP considering probability of mal‐operation of tie‐switch between SH and grid. In this regard suggested algorithm provides consumer risk reduction in SH EMS problem regarding to uncertainty of market price. It is so crucial that all of triple uncertainties of PV and WT resources and tie switch mal‐operation are considered as residential consumer risk constraints to achieve accurate results. Genetic algorithm (GA) is used as optimization method for solving of risk‐based SH EMS problem. Proposed EMS algorithm is implemented for test SH via simulation studies using MATLAB software. Results indicate presented risk‐based GA increases the thermal and energy storage by 20.25% and 14.28% and reduces the consumer risk when a blackout occurs by increasing the spinning reserve.

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ρ_{i}^{buy} = \sum_{i = 1}^{N} ((), {OF}_{SH}^{EMS}) ∙ ρ_{(i - 1)}^{buy} + ε_{i} - \sum_{i = 1}^{N} E_{i}^{Market} ε_{(i - 1)},

where the

ρ_{i}^{buy}

is the energy price,

{OF}_{SH}^{EMS}

is the total cost of SH,

E_{i}^{Market}

is the purchase energy of SH, in the interval of i th.…”

Section: Numerical Studies and Simulation Resultsmentioning

confidence: 99%

Arisk‐basedenergy management system design for grid‐connectedsmart homes

Mohammadi

Faghihi

Kazemi

et al. 2021

Int Trans Electr Energ Syst

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show abstract

“…To reduce the computational burden, a very large set of generated scenarios are reduced appropriately without losing the stochastic properties significantly. In the proposed approach, the scenario-reduction algorithm reduces and bundles the scenarios using the Kantorovich distance matrix [44]. The probability of all the deleted scenarios is assumed zero, while the new probabilities of the preserved scenarios are equal to the sum of their former probabilities and the probabilities of the deleted scenarios that are closest to them.…”

Section: ) Scenario Reductionmentioning

confidence: 99%

An Integrated Energy Management Approach for the Economic Operation of Industrial Microgrids Under Uncertainty of Renewable Energy

Mohy-ud-din

Vu²,

Muttaqi

et al. 2020

IEEE Trans. on Ind. Applicat.

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© 1972-2012 IEEE. Many modern industries are equipped with onsite renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available onsite renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity bill to have a huge saving in the electricity cost. This article proposes an energy management system for such an industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrids subject to the scalability of the BESS under uncertainties. The proposed optimization involves two stages. In the first stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second stage, it minimizes the cost of the microgrid operation based on the decision made in the first stage. This proposed two-stage energy management strategy is formulated as a single-stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition, the proposed strategy also considers the various operating limits of the energy storage, such as the efficiency and the charging and the discharging rates, and considers the fading effect of the batteries of the BESS. The proposed strategy is then validated using two typical datasets from two different industrial units in New South Wales, Australia. The simulation results show that the proposed strategy 1 Abstract-Many modern industries are equipped with on-site renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available on-site renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity bill to have a huge saving in the electricity cost. This paper proposes an energy management system for such an industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrids subject to the scalability of the BESS under uncertainties. The proposed optimization involves two stages. In the first stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second stage, it minimizes the cost of the microgrid operation based on the decision made in the first stage. This proposed two-stage energy management strategy is formulated as a single stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition...

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“…In Section 3.4, we describe how we exploit every price scenario to build a step-wise bid curve. Many methods have been described in the literature that can produce reliable and accurate scenarios for market prices [27][28][29].…”

Section: Wind and Price Uncertainty Characterisationmentioning

confidence: 99%

Hybrid uncertainty‐based offering strategy for virtual power plants

Alahyari

Ehsan

Pozo

et al. 2020

IET Renewable Power Generation

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This study proposes an optimal day-ahead (DA) electricity market offering model for a virtual power plant (VPP) formed by a mix of renewable distributed energy resources along with energy storage, such as electric vehicles. Two sources of uncertainty are considered, namely, wind power generation, modelled by an uncertainty set, and DA market price, modelled by scenarios. Opposite to classical robust optimisation approaches, the authors model maps minimal (worst-case) profits to a conservativeness parameter, while the classical robust optimisation maps conservativeness parameter to worst-case profits. In this regard, by using their optimisation framework, a VPP operator only deals with setting a minimum-profit constraint, which is more sensible and easy for interpretation, while the required conservativeness is endogenously determined. The proposed mathematical model for constructing the offering curve is a hierarchical four-level robust optimisation problem. The first level represents the optimal decision on the price-quantity offer bids; the second-and third-level relate to the optimal identification of conservativeness parameter; and the fourth-level represents the optimal operation of the VPP managed assets. The four-level model is reformulated as a single-level mixed-integer linear programming problem. The proposed approach and its applicability are verified using numerical simulations.

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Scenario based uncertainty modeling of electricity market prices

Cited by 8 publications

References 10 publications

Arisk‐basedenergy management system design for grid‐connectedsmart homes

Arisk‐basedenergy management system design for grid‐connectedsmart homes

An Integrated Energy Management Approach for the Economic Operation of Industrial Microgrids Under Uncertainty of Renewable Energy

Hybrid uncertainty‐based offering strategy for virtual power plants

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