Abstract
The concept of Carrier-Based Demand Response (CBDR) programs in Smart Multi-Energy Systems (MES) is proposed in this paper. It is discussed that by establishing the bi-directional relation between multi-energy demand and MES through the penetration of multi-carrier device technologies, the opportunity of demand-side participation in system operation can be activated. In this paper, the external dependency caused by multi-carrier devices is employed as a demand response. The CBDR is introduced as the flexibility of end-use to change the conversion pattern of input carriers into required demand. As the CBDR program is influenced by energy carrier prices, upstream network obligations and also the customer's behavior, its uncertainty is effectively modeled in this paper. The results compare the difference between the stochastic and deterministic approaches to the problem and show the improved accuracy through the stochastic modeling. The role of those customers that are not taking part in CBDR program is also investigated.

Abstract
A recent solution to tackle environmental issues is the electrification of transportation. Effective integration of plug-in electric vehicles (PEVs) into the grid is important in the process of achieving sustainable development. One of the key solutions regarding the need for charging stations is the installation of PEV parking lots (PLs). However, contrary to common parkings, PLs are constrained by various organizations such as municipalities, urban traffic regulators, and electrical distribution systems. Therefore, this paper aims to allocate PLs in distribution systems with the objective of minimizing system costs including power loss, network reliability, and voltage deviation as possible objectives. A two-stage model has been designed for this purpose. PLs' behavior considering market interactions is optimized at the first stage to provide profit to the PL owner. At the second stage, the PL allocation problem is solved considering various network constraints. Conclusions are duly drawn with a realistic example.

Abstract
Many efforts are being devoted towards achieving optimal planning and operation of DER (Distributed Energy Resources). However, during the planning process, not all relevant thermal constraints of the distribution network are considered; some works claim that they must be taken into account, while others follow the single-node approach. This paper assesses the effects of the distribution network thermal constraints in DER planning, using a deterministic linear programming problem to find the optimal DER planning and operation. Three case studies with different network topologies under several DER implementation scenarios are analyzed. A DC load flow is used to estimate the required network reinforcements to accommodate optimal DER investments, if any. Reinforcement costs are then calculated to assess the net benefit compared to limiting DER investments and operation, according to the network thermal limits. Results suggest that there is no significant economic advantage in limiting DER investments and line flows, compared to reinforcing the low voltage network to allow the larger flows that result from an unconstrained network problem.

Abstract
Despite the fact that reserves still have a small impact on the final electricity price, the rapid irruption of renewable and interruptible technologies has put in the spotlight the value of these services. It seems therefore important to rely on market models able to output realistic energy and reserve prices under imperfect competition. However, few are the authors that have modeled strategic behavior in both commodities. This paper presents an hourly multi-period oligopolistic model for energy and reserve markets, with units' commitment decisions and hydro-coordination, based on the conjectural supply function equilibrium. Its outputs have been compared with real Spanish data from the first weeks of 2011 with satisfactory results. © 2015 IEEE.

Abstract
Net Present Value (NPV), Weighted Average Cost of Capital (WACC), Internal Rate of Return (IRR), and Total-Life Cost of Capital (TLCC) are economic concepts widely used in capital budgeting to measure and compare the profitability of investments. More specifically, in the electricity sector these measures, with the Levelized Cost of Energy (LCOE), are very often used to assess investments in generation assets. At the same time, electricity generation models based on mathematical programming and game theory have also been developed to determine optimal expansion plans of the generation capacity for long-term horizons. Though these two techniques have both been applied in the literature to assess generation investments in the electricity sector, taking into account, among others, investment, maintenance and operation costs, their mathematical relationships have been rarely reported or even understood. Here we provide some insight on the mathematical links existing between both approaches. © 2015 IEEE.

Abstract
Distributed Generation (DG) is providing end consumers the possibility to satisfy part of their electricity consumption by using their own small-scale power generators. To regulate DG, new regulation schemes are needed, being net metering one of the most used. However, regulators appreciate that net metering could jeopardize the incomes to cover the regulated activities' cost. This paper proposes a mathematical bi-level model to obtain the evolution of the access tariffs and their corresponding incomes needed to cover the regulated costs, as well as the optimal DG investment of the consumers under a net metering regulation, in a simplified framework. © 2015 IEEE.