Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EVs) and renewable energy sources (RES). EVs introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS and EV charging stations (EVCS) are jointly considered. The charging demand necessary for EVs transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the k-means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system.

The first part of this two-paper series describes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for isolated systems. The role of DR and ESS has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR and ESS on an equal footing. The model presented includes DR and ESS in the planning of insular distribution systems. Hence, this paper presents a novel model to decide the joint expansion planning of distributed generation and the distribution network considering the impact of ESS and price-dependent DR programs. The problem is formulated as a stochastic-programming-based model driven by the maximization of the net social benefit. The associated deterministic equivalent is formulated as a mixed-integer linear program suitable for commercially available software. The outcomes of the model are the location and size of new generation and storage units and the distribution assets to be installed, reinforced or replaced. In the second companion paper, an insular case study (La Graciosa, Canary Islands, Spain) is provided illustrating the effects of DR and ESS on social welfare.

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented.

The coordination of various energy vectors under the concept of multi-energy system (MES) has introduced new sources of operational flexibility to system managers. In this paper, the behavior of multi-energy players (MEP) who can trade with more than one energy carrier to maximize their profits and mitigate their operational risks has been investigated. The MES is represented based on a multilayer structure, namely the energy market, MEP, the local energy system (LES), and multi-energy demand. In such environment, an MEP aggregates LES and participates in the wholesale electricity market, simultaneously to maximize its profit. The decision-making conflict of the MEP with other energy players for the aggregation of LES and participation in the electricity market is modeled based on a bilevel approach. Numerical results show the behavior of the MEP as a prosumer in the electricity market to produce smoother demand and price profiles. Results reveal a mutual effect of local and wholesale equilibrium prices by increasing the share of the MEP.

A bilevel stochastic programming problem (BSPP) model of the decision-making of an energy hub manager is presented. Hub managers seek ways to maximize their profit by selling electricity and heat. They have to make decisions about: (1) the level of involvement in forward contracts, electricity pool markets, and natural gas networks and (2) the electricity and heat offering prices to the clients. These decisions are made under uncertainty of pool prices, demands as well as the prices offered by rival hub managers. On the other hand, the clients try to minimize the total cost of energy procurement. This two-agent relationship is presented as a BSPP in which the hub manager is placed in the upper level and the clients in the lower one. The bilevel scheme is converted to its equivalent single-level scheme using the Karush-Kuhn-Tucker optimality conditions although there are two bilinear products related to electricity and heat. The heat bilinear product is replaced by a heat price-quota curve and the electricity bilinear product is linearized using the strong duality theorem. In addition, conditional value at risk is used to reduce the unfavorable effects of the uncertainties. The effectiveness of the proposed model is evaluated in various simulations of a realistic case study.