Optimal Allocation of ESS in Distribution Systems With a High Penetration of Wind Energy

Atwa, Y.M.; El-Saadany, Ehab F.

doi:10.1109/tpwrs.2010.2045663

Cited by 424 publications

(238 citation statements)

References 19 publications

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“…6 showed that whereas strategy (c) was more effective than strategy (b) when measured by social welfare, measuring the effectiveness by the amount of wind power curtailment would give the opposite conclusion. This demonstrates how the criteria of minimizing the DG curtailment or maximizing the utilization of DG, which is sometimes used in the literature as a performance measure [7,23,36], is not necessarily giving the best operational strategy from the perspective of the distribution system [13]. The implication of this result is that a using the amount of wind power curtailment may be misleading as metric for the effectiveness of the ESS operational strategy.…”

Section: Discussionmentioning

confidence: 99%

“…A number of similar models have been reported in the literature in recent years; see e.g. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. A typical problem these models are set out to solve is determining the optimal scheduling of energy storage charging/discharging over a planning horizon, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In the model presented in the present paper, we consider an additional aspect that to the best of our knowledge is not explicitly considered in the literature, namely the value of stored energy at the end of the planning horizon. The conventional approach is to require periodic boundary conditions for the planning horizon so that the amount of stored energy after the last time step has to be equal to, or larger than, the initial amount of stored energy [7][8][9][10][11][12][13][14][15][16][17]. Reference [18] instead includes a penalty function in the objective function that is proportional to the deviation of the amount of stored energy from the maximal energy capacity, promoting a fully charged energy storage at the final time step.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Valuation of stored energy in dynamic optimal power flow of distribution systems with energy storage

Sperstad

Helseth

Korpås

2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-Dynamic optimal power flow (DOPF) models are needed to optimize the operation of a power system with energy storage systems (ESSs) over an extended planning horizon. The optimal storage level at the end of each planning horizon depends on the possible realization of uncertainties in future planning horizons. However, most DOPF models simply require that the storage levels at the end and at the beginning of the planning horizon should be equal. In this paper we consider an AC DOPF model for a distribution system with ESS that explicitly takes into account the expected future value of stored energy. We illustrate the evaluation of the future value function for a system with a wind power plant and demonstrate the use of this value function in the operation of the ESS. The results show that such an operational strategy can be effective compared to not considering the value of stored energy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Valuation of stored energy in dynamic optimal power flow of distribution systems with energy storage

Sperstad

Helseth

Korpås

2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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show abstract

“…In the previous section, the round trip efficiency of the SB was considered to be 75%. However, the efficiency of present commercially available BESS technologies can vary considerably, between 60% and 96% depending on the storage technology [4,[6][7][8]14,22,25,[32][33][34][35][36][37]. In this context, the following figure shows (a) how the cost boundary moves as the efficiency of the battery changes and (b) the cost reductions for the DN when considering the pairs of power and energy costs at the borderlines.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications

et al. 2017

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Abstract:In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance. The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective. From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox.

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“…However, the structure of distribution networks currently is becoming more complex and cannot be modeled as a passive node because of the demand response and distributed generators (DGs) [12]. Furthermore, distribution networks transport electricity from DGs instead of transmission systems to end customers, which not only reduce distribution network usage cost but also potentially avoid congestion in transmission systems [13][14][15][16][17][18][19][20][21][22]. In [15], the optimal objectives are maximizing the power usage from DGs and the power usage costs of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization of smart distribution networks considering real-time pricing

Zhang

Zhao

et al. 2014

J. Mod. Power Syst. Clean Energy

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With the development of smart meters, a realtime pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

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Optimal Allocation of ESS in Distribution Systems With a High Penetration of Wind Energy

Cited by 424 publications

References 19 publications

Valuation of stored energy in dynamic optimal power flow of distribution systems with energy storage

Valuation of stored energy in dynamic optimal power flow of distribution systems with energy storage

Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications

Economic optimization of smart distribution networks considering real-time pricing

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