Demand Response-Based Operation Model in Electricity Markets With High Wind Power Penetration

Hajibandeh, Neda; Shafie‐khah, Miadreza; Talari, Saber; Dehghan, Shahab; Amjady, Nima; Mariano, S.J.P.S.; Catalào, João P. S.

doi:10.1109/tste.2018.2854868

Cited by 33 publications

(27 citation statements)

References 25 publications

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“…This literature illustrated that the independently owned BESS could submit bids/offers to participate in the energy and spinning reserve markets during both charging and discharging cycles. The market-based DR and the comprehensive evaluation of DR's roles in the future electricity markets to mitigate the variability nature of wind energy aiming to maximize system security, and reduce the total operational cost was presented in [13].…”

Section: B Literature Reviewmentioning

confidence: 99%

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

et al. 2020

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This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in realtime. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

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Section: B Literature Reviewmentioning

confidence: 99%

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

et al. 2020

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“…DR, as explained by the authors of [22], can be divided in two groups: DR-based on prices (PBDR), and DR-based on incentives, (IBDR) programs. The IBDR programs are classified into three groups: voluntary effort, the market clearing programs, and the price based programs.…”

Section: Benefits and Risks Of A Smart House And Demand Response Conceptmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

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“…The constraints (12) define the generation-demand balance at every node, every hour and every scenario. Equations (13) and (14) define the power flows through non-dispatchable and dispatchable transmission assets, respectively, which are limited by constraints (25). Constraints (15) define binary variables zl,s,t that indicate whether a dispatchable line is in service (zl,s,t = 1) or out of service (zl,s,t = 0).…”

Section: Balancing Stage Constraintsmentioning

confidence: 99%

“…The model provided in the previous section is nonlinear due to the products of binary variables z and continuous variables δ in constraints (4) and (14). However, it is possible to replace these nonlinear constraints with the following sets of exact equivalent mixed-integer linear constraints:…”

Section: Mixed-integer Linear Programming (Milp) Modelmentioning

confidence: 99%

“…A demand response (DR) model is introduced in [11], [12] in which authors proposed an optimization framework for the DR aggregation in wholesale electricity markets and day-ahead markets. Taking into account of joint wind power sources and demand-side participation in electricity market operation is considered in [13] and [14] in which a two-stage optimization approach pertaining to the scenarios is employed. Nevertheless, transmission lines are presumed to be uncontrollable in the aforementioned papers.…”

Section: Introductionmentioning

confidence: 99%

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Co-optimization of transmission topology and generation dispatch incorporating demand response and renewable energy uncertainty in day-ahead electricity markets

Năng¹

2019

jst

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The variability of renewables requires a higher degree of flexibility in power system operation. At present, there are a variety of solutions which are being utilized, particularly demand response and transmission switching. This paper presents a model for co-optimization of transmission topology and generation dispatch based on a two-stage stochastic optimization. Demand response and renewable energy uncertainty are integrated into the proposed model. The uncertainty pertaining to renewable energy sources is presented through a set of scenarios. The model is a mixed-integer linear programming (MILP) problem and can be applied for the day-ahead market clearing. The results implemented using a 5-bus system demonstrate the effectiveness of the proposed model.

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Demand Response-Based Operation Model in Electricity Markets With High Wind Power Penetration

Cited by 33 publications

References 25 publications

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

Analysis Application of Controllable Load Appliances Management in a Smart Home

Co-optimization of transmission topology and generation dispatch incorporating demand response and renewable energy uncertainty in day-ahead electricity markets

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