Strategic and network-aware bidding policy for electric utilities through the optimal orchestration of a virtual and heterogeneous flexibility assets’ portfolio

Steriotis, Konstantinos; Smpoukis, Konstantinos; Efthymiopoulos, Nikolaos; Τσαούσογλου, Γεώργιος; Makris, Prodromos; Varvarigos, Emmanouel

doi:10.1016/j.epsr.2020.106302

Cited by 28 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The potential of strategic behaviour by aggregators has been demonstrated in recent studies [11], [12]. In [13], it was also shown that even small aggregators can manipulate the price by leveraging their grid location. Moreover, in [14], deep reinforcement learning is shown to be a well-performing method for aggregators to compute such a strategy.…”

Section: Introduction and Related Workmentioning

confidence: 91%

“…As explained above, c i = c i ( K i , L i ) is the observation of the actual flexibility costs for aggregator i after the end of the horizon, while K i is the measured flexibility factor, which is calculated at the end of the horizon based on the measurements of the actual power injections. The first term in (13) compensates the aggregator for its actual flexibility cost that the latter offered at its flexible assets. The second term is the worst-off flexibility cost among aggregators, where the cost is calculated over the declared tuples L j and instruction factors K * j of the rest of the aggregators, j = i, but over Let us temporarily assume that all aggregators follow their instruction…”

Section: Reward Function Designmentioning

confidence: 99%

“…Lemma 1. If the aggregators always follow the instruction, then under the CPM payment rule (13), it is a dominant strategy for each aggregator to declare its true parameters…”

Section: Reward Function Designmentioning

confidence: 99%

See 2 more Smart Citations

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Τσαούσογλου

Giraldo

Paterakis

2021

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

Section: Introduction and Related Workmentioning

confidence: 91%

Section: Reward Function Designmentioning

confidence: 99%

See 1 more Smart Citation

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Τσαούσογλου

Giraldo

Paterakis

2021

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

“…With payment rule (13), the aggregator's payoff π i from (12), considering K i = K * i is given by…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Τσαούσογλου¹,

Giraldo²,

Paterakis³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fair formulation is proposed for the allocation of flexibility. Since the DSO is not aware of each aggregator's local flexibility costs, we show that strategic misreporting can lead to severe loss of efficiency. Using mechanism design theory, we provide a mechanism that makes it a payoff-maximizing strategy for each aggregator to make truthful bids to the flexibility market. While typical truthful mechanisms only work when the objective is the maximization of Social Welfare, the proposed mechanism lets the DSO achieve incentive compatibility and optimality for the the max-min fairness objective.

show abstract

“…Loads are located on nodes 1, 2, 3, 4, 6, 7, 10, 11 and 12 of the DN. Load and line data for the DN are based on data in [24] and can be found in our recent work in [25]. We discretize the time horizon into 24 hourly timeslots.…”

Section: Simulation Setupmentioning

confidence: 99%

Network and Market-Aware Bidding to Maximize Local RES Usage and Minimize Cost in Energy Islands with Weak Grid Connections

et al. 2020

Self Cite

View full text Add to dashboard Cite

The increasing renewable energy sources RES penetration in today’s energy islands and rural energy communities with weak grid connections is expected to incur severe distribution network stability problems (i.e., congestion, voltage issues). Tackling these problems is even more challenging since RES spillage minimization and energy cost minimization for the local energy community are set as major pre-requisites. In this paper, we consider a Microgrid Operator (MGO) that: (i) gradually decides the optimal mix of its RES and flexibility assets’ (FlexAsset) sizing, siting and operation, (ii) respects the physical distribution network constraints in high RES penetration contexts, and (iii) is able to bid strategically in the existing day-ahead energy market. We model this problem as a Stackelberg game, expressed as a Mathematical Problem with Equilibrium Constraints (MPEC), which is finally transformed into a tractable Mixed Integer Linear Program (MILP). The performance evaluation results show that the MGO can lower its costs when bidding strategically, while the coordinated planning and scheduling of its FlexAssets result in higher RES utilization, as well as distribution network aware and cost-effective RES and FlexAsset operation.

show abstract

Strategic and network-aware bidding policy for electric utilities through the optimal orchestration of a virtual and heterogeneous flexibility assets’ portfolio

Cited by 28 publications

References 27 publications

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Mechanism Design for Fair and Efficient DSO Flexibility Markets

Network and Market-Aware Bidding to Maximize Local RES Usage and Minimize Cost in Energy Islands with Weak Grid Connections

Contact Info

Product

Resources

About