Abstract
This paper presents a methodology for the optimization of bilateral point-to-point power transactions between prosumers/electricity consumers, taking into account the physical constraints of the power grid, aiming to minimize the total cost of electricity expenditures. As a resolution strategy, the optimization problem is divided into two stages. The first consists of a purely energy problem, in which prosumers/consumers establish energy exchanges in an efficient manner, in order to obtain an adequate balance between demand and total system generation. The second stage is physical, in which the grid constraints are modeled, such as maximum line loading and bus voltage level. A 14-bar system with 19 peers (agents) that also considers the possibility of switching with an external grid, which can be another set of peers, another energy community, or simply the grid under the responsibility of the local utility, is modeled for the evaluation of the proposed methodology. The proposed method proved to be effective because it achieved results where the operation of the electric grid is feasible.

Abstract

Abstract

Abstract

Abstract
The increasing penetration of renewable electricity generation is complicating the bidding and estimating processes of electricity prices, partly due to the shift of the overall cost sensitivity from operation (fuel) costs to investment costs. However, cost minimization models for capacity expansion are frequently based on the principle that, for a perfectly adapted system allowing non-served energy, marginal remuneration allows overall operation and investments costs recovery. In addition, these models are usually formulated as finite-horizon problems when they should be theoretically solved for infinite horizons under the assumption of companies' infinite lifespan, but infinite horizon cannot be dealt with mathematical programming since it requires finite sets. Previous approaches have tried to overcome this drawback with finite horizon models that tend asymptotically to the original infinite ones and, in many cases, the investment costs are annualized based on the plants' lifespan, sometimes including a cost residual value. This paper proposes a novel approach with a finite horizon that guarantees the investment costs' recovery. It is also able to obtain the marginal electricity costs of the original infinite horizon model, without the need for residual values or non-served energy. This new approach is especially suited for long-term electricity pricing with investments in renewable assets when non-served demand is banned or when no explicit capacity remuneration mechanisms are considered.

Abstract
This paper presents a conjectured-price-response equilibrium approach for modeling both centralized generation (CG) and behind-the-meter distributed generation (BMDG). A Nash game is set up with two constraints linking the CG and BMDG decisions to satisfy both the electricity demand in an energy market and the firm capacity in a capacity market. CG agents maximize their market profits while BMDG customers minimize their net supply costs, making decisions on their annual capacity investments and hourly productions decisions. Customers' costs account for 1) the energy bought from the grid minus the BMDG energy surpluses sold; 2) the payment of the grid access tariff (power and energy-based terms) and 3) the BMDG capacity investments' costs. The equilibrium conditions enable to represent different degrees of oligopoly using conjectural variations in both the energy and capacity markets. This work proves that such an equilibrium problem can be solved through an equivalent, yet simpler-to-solve, quadratic minimization problem. Some case examples compare the results of the proposed joint energy and capacity equilibrium with those from an energy-only equilibrium. Among other conclusions, these cases show that the proposed equilibrium sends adequate economic signals to the consumers to taper off the total system peak demand, whenever the weight of the power-based term of the access tariff is not extremely high.