Improvement of Demand Side Management and Social Welfare Index Using a Flexible Market-Based Approach

Sahraie, Elahe; Zakariazadeh, Alireza; Gholami, M.

doi:10.1109/tia.2019.2936389

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The remarkable values of the MPR index and also the NSRR index which has the value of 100% under all weighting strategies are because of the separate clearing constraints which is used in the proposed framework. According to the policy of the separate clearing framework, all market participants should earn net surplus commensurate with the value of the power supplying. This feature of the separate clearing constraints leads to detoxifying the problems of the uniform pricing (UP) method in terms of profiting to all the power suppliers even to the marginal generators (generators which their proposed prices for power compensation is equal with the market clearing price).…”

Section: Analysis Of the Results Of The Case Studymentioning

confidence: 99%

“…According to Table , as it was expected, the minimum value of the GSR index occurred in scenario 5 of UW with ( w GSR = 96%), which shows the proper controlling of the dispersion of the reactive power compensation with directly controlling the level of the GSR index under the proposed framework. To better assess the competitive level of the SRPM under the proposed framework, the coefficient of variation (CV) indices introduced are measured for the SRPM under the proposed framework. These indices formulated in Equations (34)‐(36) has been defined as the ratio of SD (Std) to the mean (M) value of varieties to model the dispersion of power suppliers in terms of the net surplus, reactive compensation and revenue, respectively.…”

Section: Analysis Of the Results Of The Case Studymentioning

confidence: 99%

“…In this paper, separate clearing (SC) is used to clear the SRPM. The SC is formulated as Equations (23a)‐(23d) . According to Equations (23a)‐(23d), the SC clearing constraints assigns the MCPs for reactive power compensation per each power supplier in each zone of the reactive power compensation.

{B_{0}^{i, u} a}_{0}^{i, u} = C_{0}^{i, u}

B_{1}^{i, u} M_{1}^{i, u} < C_{1}^{i, u}

((), B_{2}^{i, u} + B_{3}^{i, u}) M_{2}^{i, u} < C_{2}^{i, u}

B_{3}^{i, u} M_{3}^{i, u} < C_{3}^{i, u}

…”

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Development of a multi‐objective framework for the separate active and reactive power market clearing with index‐based vision

Sahraie

Zakariazadeh

Gholami

2019

Int Trans Electr Energ Syst

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Summary In this paper, a new multi‐objective power market clearing framework with index‐based vision is proposed in order to flexibly cover various aspects of the power market, simultaneously. Separate clearing constraints and a weighting strategy are also implemented along with the proposed clearing framework aiming at considering the importance of the power market and network‐based indices. Also, a number of applicable indices are integrated into the objective functions. These indices which help with improving the quality of the power market clearing are to control on the market total cost, market profitability rate, discrimination among market players, dispersion in the net surplus and revenue among market players, players participation tendency in the power market, and the market power potential. Moreover, voltage stability constraints have been introduced to improve the proposed framework. The proposed framework is used to clear the separate reactive power market (SRPM) as a case study and is tested on 24‐bus IEEE reliability test system (RTS). The problem is solved by a non‐dominated sorting genetic algorithm (NSGA‐II). The results evidence that the proposed market clearing framework significantly improves the performance of the power market clearing.

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Section: Analysis Of the Results Of The Case Studymentioning

confidence: 99%

Section: Analysis Of the Results Of The Case Studymentioning

confidence: 99%

{B_{0}^{i, u} a}_{0}^{i, u} = C_{0}^{i, u}

B_{1}^{i, u} M_{1}^{i, u} < C_{1}^{i, u}

((), B_{2}^{i, u} + B_{3}^{i, u}) M_{2}^{i, u} < C_{2}^{i, u}

B_{3}^{i, u} M_{3}^{i, u} < C_{3}^{i, u}

…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Development of a multi‐objective framework for the separate active and reactive power market clearing with index‐based vision

Sahraie

Zakariazadeh

Gholami

2019

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…The evolutionary algorithm used in this paper (NSGA-II) was proposed by Deb et al [7], and since then several authors have been using this algorithm to solve different multi-objective optimization problems [15], particularly in the power system field [8,16]. Figure 2 displays the flowchart of the NSGA-II algorithm.…”

Section: Hybrid Evolutionary Algorithmmentioning

confidence: 99%

Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm

et al. 2020

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The introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).

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“…According to a general classification, physical withholding and financial withholding recognized as two underlying factors in this regard. For instance, 8 investigated collision in form of both financial and physical withholding in different load levels, authors in Reference 9 maximized the energy storage profit by capacity withholding and 10 compered market power implemented thorough capacity withholding in three types of market: oligopoly market, perfectly competitive market, and combination of these two. However, it is possible to calculate the effectiveness of each of these factors through market power indices.…”

Section: Introductionmentioning

confidence: 99%

Proposing new analytical price‐based market power and coalition indices based on LMP share in double‐sided power market

Saghravanian

Samadi

Hajiabadi

2021

Int Trans Electr Energ Syst

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One of the strategic actions to gain more profit in the electricity market is to exercising market power as a leverage in exclusive situations. The main goal of this article is to assess load serving entities market power via self-action scramble by price-based indices as an application of the structural decomposition approach.In addition, the possibility of forming a beneficial coalition due to the cross interactions between players can be evaluated using the proposed indices. These coalitions may be occurred between two generation units, two load serving entities, or a generation unit and a load serving entity. The cumulative sharing impacts of one load serving entity on the locational marginal prices of all units owned by a GenCo is also defined as a new index. To achieve this goal, the first step is to extract the structural decomposition coefficients of market components consisting of suppliers, inelastic, and elastic loads, which determine the contribution of all players on the electricity price separately. In the second step, new structural indices to determine self/cross-share index of each player in the electricity price based on the contributing factors obtained from the decomposition process. So, it is possible for market operator to prioritize the potential of exercising market power and forming coalition with a price-based index for both-sides players. As well, it is important for market operators to explore the economic justification of relationships between players to prevent market deviation. The proposed approach is evaluated on the IEEE 24-bus system, and the results are discussed.List of Symbols and Abbreviations: SG, set of all generators; D, set of all demands; L, set of all transmission lines; N G :N G , set of upper and lower bounded generators; N G , set of marginal generation units; N D , set of all fully dispatch elastic loads; N D , set of all partial dispatch elastic loads; L cong , set of all congested lines; n, bus number; a i , intercept of bid function for generator i; b i , slope of bid function for unit i; c j , intercept of offer function for load j; d j , slope of offer function for load j; P max l , upper flow limits of line l; P max g , P min g , upper/lower active power limit of generators; P max e , P min e , upper/lower active power limit of LSEs; P inelastic j , the active power of jth inelastic load; P d , total dispatched active power consist of elastic and inelastic load; P frc , total forecasted active power load; P g i , the active power generated by ith unit; P ej , the active power of jth LSE; α l:i , power transmission distribution factor of line l with respect to node ith generator; α l:j , power transmission distribution factor of line l with respect to node jth load; λ, Lagrange multiplier of the equality constraint; μ max i , μ min i , Lagrange multiplier of maximum/minimum generation limits refer to ith unit; Γ max l , Γ min l , Lagrange multiplier of the upper/lower of line flow limits refer to lth transmission line; ω max n , ω min n , Lagrange multiplier o...

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Improvement of Demand Side Management and Social Welfare Index Using a Flexible Market-Based Approach

Cited by 11 publications

References 17 publications

Development of a multi‐objective framework for the separate active and reactive power market clearing with index‐based vision

Development of a multi‐objective framework for the separate active and reactive power market clearing with index‐based vision

Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm

Proposing new analytical price‐based market power and coalition indices based on LMP share in double‐sided power market

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