Network investment planning for high penetration of wind energy under demand response program

Kazerooni, Ali; Mutale, Joseph

doi:10.1109/pmaps.2010.5528517

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…In addition, DSM can provide various other benefits to electrical energy systems and markets with renewable energy, such as reducing price spikes and the average spot price [31], shifting market power from generators to consumers [37], replacing or postponing infrastructure expansion [37,39,40], reducing use of costly peak power [37] and reducing transmission and distribution losses [41]. DSM may also facilitate energy efficiency measures [42,43].…”

Section: Overviewmentioning

confidence: 99%

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Lund

Lindgren

Mikkola

et al. 2015

Renewable and Sustainable Energy Reviews

1,252

578

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Title: Review of energy system flexibility measures to enable high levels of variable renewable electricity Article Type: Review Article Keywords: energy system flexibility; DSM; energy storage; ancillary service; electricity market; smart grid Abstract: The paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different "P2Y"-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To "functionalize" or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options available seems to be promising. AbstractThe paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different -P2Y‖-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To -functionalize‖ or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options avai...

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Section: Overviewmentioning

confidence: 99%

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Lund

Lindgren

Mikkola

et al. 2015

Renewable and Sustainable Energy Reviews

1,252

578

View full text Add to dashboard Cite

show abstract

“…Authors in [19] incorporate DR in a 4-week economic dispatch approach and extrapolate the results to one year. In [20], a network investment planning model for a high penetration of wind energy under a DR program is presented. In [21], the effects of DR on generation expansion planning in restructured power systems are modeled.…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model

Asensio

Quevedo

Muñoz-Delgado

et al. 2018

IEEE Trans. Smart Grid

187

107

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The first part of this two-paper series describes the incorporation of Demand Response (DR) and Energy Storage Systems (ESS) in the joint distribution and generation expansion planning for isolated systems. The role of DR and ESS has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR and ESS on an equal footing. The model presented includes DR and ESS in the planning of insular distribution systems. Hence, this paper presents a novel model to decide the joint expansion planning of Distributed Generation (DG) and the distribution network considering the impact of ESS and price-dependent DR programs. The problem is formulated as a stochastic-programming-based model driven by the maximization of the net social benefit. The associated deterministic equivalent is formulated as a mixed-integer linear program suitable for commercially available software. The outcomes of the model are the location and size of new generation and storage units and the distribution assets to be installed, reinforced or replaced. In the second companion paper, an insular case study (La Graciosa, Canary Islands, Spain) is provided illustrating the effects of DR and ESS on social welfare.

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“…For example, in Australia, $12-$18 billion were spent on transmission lines (TL) that remained inactive and were used for only about 90 h a year [4], which should be considered as a waste of financial resources. Therefore, TEP should take into account the demand resources, including narrowly concentrated interruptible loads and energy efficiency measures [14], rather than loads that can respond to sustain prices at any time. Thus, in [8,9], a probabilistic structure was introduced for a multi-objective TEP for the behaviours of large-scale remote wind farms.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic transmission expansion planning considering distributed generation and demand response programs

Hejeejo

Qiu

2017

IET Renewable Power Generation

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Transmission expansion planning (TEP) is generally determined by peak demands. To improve the efficiency and sustainability of energy systems, attention has been paid to demand response programs (DRPs) and distributed generation (DG). DRPs and DG will also have significant impacts on the controllability and economics of power systems, from short-term scheduling to long-term planning. In this study, a non-linear economic design for responsive loads is introduced, based on the price flexibility of demand and the customers' benefit function. Moreover, a probabilistic multi-objective TEP model which considers DRPs is also proposed. A probabilistic analysis method, the so-called Monte-Carlo simulation method, is implemented to handle the uncertainty of the loads, DRPs and DG in the TEP problems. Due to the problems' non-convex formulations, a non-dominated sorting differential evolution program is used to solve the TEP problems. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The planning methodology is then demonstrated on an IEEE 118-bus system in order to show the feasibility of the proposed algorithm.

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Network investment planning for high penetration of wind energy under demand response program

Cited by 19 publications

References 10 publications

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model

Probabilistic transmission expansion planning considering distributed generation and demand response programs

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