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...
In this paper, a new mechanism is proposed to apportion expected reserve costs between electricity market agents in the power system. The uncertainties of generation units, transmission lines, wind power generation and electrical loads are considered in this model. Hence, a Stochastic Unit Commitment (SUC) is used to apply the uncertainty of stochastic variables in the simultaneous energy and reserve marketclearing problem. Moreover, electrical customers can participate in the electricity market based on their desired strategies. In this paper, a novel method is proposed to allocate reserve costs between GenCos, TransCos, electrical customers and wind farm owners. Consequently, market agents are responsible for paying a portion of the allocated expected reserve costs based on the economic metrics that are defined for the first time in this paper. Finally, two cases including a 3-bus test system and IEEE-RTS are utilized to illustrate the performance of the proposed mechanism to share the expected reserve costs.
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