Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets

Abstract

Today, traditional networks are changing to active grids due to the burgeoning growth of distributed energy resources (DER), which demands scrupulous attention to technical infrastructures, as well as economic aspects. In this study, from economic point of view, the aggregation of DERs in a distribution network to participate in joint energy and reserve markets is investigated. This approach, which is predicated upon price-based unit commitment method, has considered virtually all the technical data in the proposed model. It is worth to mention that uncertainties of loads and market prices, as an inherent characteristic of the electricity markets, are treated in this study, and their effect on the operation of virtual power plants in energy and reserve markets has been thoroughly discussed. To this end, for both uncertain parameters, a good number of scenarios are generated and using the backward reduction method the number of these scenarios is reduced. The problem is formulated as a MINLP model and is implemented in GAMS software, while its authenticity is validated using particle swarm optimisation method. NomenclatureIndices i,j index for buses t index for hours N total number of buses Sets S DG set of distributed generation (DG) units S IL set of interruptible load (IL) S EES set of electrical energy storage (EES) S int set of tie-lines S b set of buses Variables P DG t i; Q DG t i amount of active and reactive power generated by ith DG unit at hour t for the energy market R DG t i amount of active power generated by ith DG unit at hour t for reserve marketamount of curtailed load of ith IL allotted to spinning reserve market at hour t P min DG i ; P max DG i minimum and maximum active power generation of ith DG unit P int t i ; Q int t i amount of active and reactive exchanged power between virtual power plant (VPP) and the ith neighbouring grid (positive sign for purchasing from the neighbouring grid and negative sign for selling to it) P E t ; Q E t amount of active and reactive exchanged power between VPP and upstream network (positive sign for purchasing from the neighbouring grid and negative sign for selling to it) R t sum of curtailed load and DG generation allotted to spinning reserve market at hour t P Load t ; Q Load t supplied active and reactive load by VPP to end customers P ch t i ; P Dch t i amount of power charged/discharged into ith EES at hour t P Str t i amount of charged/discharged capacity of ith EES at hour t in kW (positive sign for charging state and negative sign for discharging state) P min Str ; P max Str minimum and maximum capacity of ith EES in kWh R ch i ; R Dch i maximum charge/discharge rate of ith EES in kW Cap t i state of charge of the ith EES at hour t C(P DG t i ) generation cost function of ith DG unit C(P Str t i ) operation cost function of ith EES C(P IL t i )contracted cost function of IL to curtail its load in specified hours I t ; L t ; J i,t ; K i,t ; I t i binary variables V i,t /d i,t voltage phasor at bus i at hour t Y ij /u ij polar form of ijth element of a...

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

“…The proposed ENNC has the advantages of ANNC in addition to optimizing the payoff matrix to attain the optimal ranges of the uncertainty horizons as well as optimizing the anchor points to provide a more effective utopia hyperplane. Due to these reasons, the proposed ENNC has the best performance among the MOMP methods of Table III. To further evaluate the effectiveness of the proposed VPP self-scheduling strategy, it is compared with: -Two-stage stochastic programming (SP) with three different numbers of scenarios in Table IV [3], [4]. These SP methods, denoted by SP1, SP2, and SP3, have 20, 50, 100 scenarios.…”

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