# Economic Energy Allocation of Conventional and Large-Scale PV Power Plants
###### Abstract
They concluded that the total electricity cost would decrease with increasing the renewable energy sources penetration. This is due to the price reductions for both PV panels and windmills. In addition, [PERSON] et al. [11] studied the feasibility of meeting 100% of India's electricity consumption from PV by 2050. The authors developed an energetic flow model to simulate inputs and outputs of electricity from the PV system from 2016 to 2050. They found this will require considerable investment in the PV system and the associated storage infrastructure. [PERSON] et al. [12] developed a new market design for the German electricity market. They aimed to ensure energy supply security and renewable energy expansion. They concluded that conventional power plants are still needed to ensure system stability due to the intermittent nature of renewable energy sources. Furthermore, [PERSON] et al. [13] an optimization model for increasing the renewable energy penetration in a hybrid system. The proposed model concluded that increasing the prediction horizon length increases the renewable energy penetration and share. Regarding the relationship between the electricity distributors and the power plants, [PERSON] and [PERSON] [14] proposed a novel hierarchical distributed method under the Progressive Second Price (PSP) auction mechanism. The generation provider or the retailers obtain their electricity allocation through the PSP auction method by submitting a multi-dimensional bid profile instead of telling their own cost or valuation function. The retailers economically distribute the electricity acquired in the PSP auction. Moreover, the valuation function of retailers depends on the revenues that they sell the electricity to users. The auction between generation provider and retailers is a double-sided auction in which the generation provider A\({}_{0}\) act as a seller and all the retailers A\({}_{\text{i}}\) (i = 1, 2, \(\ldots\), N) act as the buyers. A\({}_{0}\) submits a (2-dimension) sell-bid profile a\({}_{0}\equiv(\alpha_{0}\), s\({}_{0})\) where \(\alpha_{0}\) is the per-unit sell-bid price, and s\({}_{0}\) is the maximum quantity offered for sale. As a buyer, each retailer A\({}_{\text{i}}\) submits a 2-dimension bid profile b\({}_{\text{i}}\equiv(\beta_{\text{i}}\), d\({}_{\text{i}})\) where \(\beta_{\text{i}}\) is interpreted as the per-unit bid price, and d\({}_{\text{i}}\) as the maximum quantity wanted. The retailer A\({}_{\text{i}}\) can then directly assign electricity for the users Ajj (j = 1, 2,..., Mj) in an economical way since the complete information of users is opened to their unique retailer. In the same context, [PERSON] et al. [15] have highlighted that large consumers could choose to purchase electricity among the following three methods:  
* Spot market
* Long-term contract trade power market
* Independent power plants. The authors studied the direct power purchase strategy for large consumers, taking electricity uncertainty in the power market into account. The purchase price is divided into annual, monthly, and online purchase prices. A direct power purchase strategy probabilistic optimization model based on a multi-state model for purchase price was proposed, and the optimization goal was minimizing the expecting purchase cost. [PERSON] et al. [16] mentioned that power distribution companies should have proper strategies to maximize profit. One of these strategies is improving and enhancing the power marketing quality. From the investors' perspective, [PERSON] et al. in [17] have proposed an optimization model to support a prospective investor in arriving at an optimal investment plan in large-scale solar photovoltaic (PV) generation projects. The optimal set of decisions includes the location, sizing, and investment time that yields the highest profit. The mathematical model considers various relevant issues associated with PV projects such as location-specific solar radiation levels, detailed investment cost representation, and approximate representation of the transmission system.