• Energy Research

This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

The integration of renewable generation requires new sources of flexibility, including the flexibility from distributed resources that can be unlocked via local flexibility markets (LFMs). In these markets, aggregators (AGGs) offer the flexibility from their portfolios to the flexibility requesting parties (FRP), i.e. system operators or other balancing requesting parties. To bid in LFMs and manage market uncertainty, AGGs must compute the flexibility they are willing to offer at each possible flexibility market price, by optimizing their portfolios. This paper proposes a 2-stage methodology to compute the flexibility bidding curve that an energy community can send to a LFM when behaving as an AGG of its members resources. At stage 1, the energy community (EC) manager computes the optimal EC operation without flexibility provision, minimizing the EC energy bill, and serving as the baseline to verify the flexibility provision. Then, at stage 2, for each possible flexibility price, the EC manager computes the optimal flexibility to be offered, minimizing the EC energy bill but including the flexibility provision incomes, to build the flexibility bidding curve. 

This paper analyzes a set of selected German and French cities' performance in terms of the relative behavior of their eco-efficiencies, computed as the ratio of their gross domestic product (GDP) over their CO2 emissions. For this analysis, eco-efficiency scores of the selected cities are computed using the data envelopment analysis (DEA) technique, taking the eco-efficiencies as outputs, and the inputs being the energy consumption, the population density, the labor productivity, the resource productivity, and the patents per inhabitant. Once DEA results are analyzed, the Malmquist productivity indexes (MPI) are used to assess the time evolution of the technical efficiency, technological efficiency, and productivity of the cities over the window periods 2000 to 2005 and 2005 to 2008. Some of the main conclusions are that (1) most of the analyzed cities seem to have suboptimal scales, being one of the causes of their inefficiency; (2) there is evidence that high GDP over CO2 emissions does not imply high eco-efficiency scores, meaning that DEA like approaches are useful to complement more simplistic ranking procedures, pointing out potential inefficiencies at the input levels; (3) efficiencies performed worse during the period 2000-2005 than during the period 2005-2008, suggesting the possibility of corrective actions taken during or at the end of the first period but impacting only on the second period, probably due to an increasing environmental awareness of policymakers and governors; and (4) MPI analysis shows a positive technological evolution of all cities, according to the general technological evolution of the reference cities, reflecting a generalized convergence of most cities to their technological frontier and therefore an evolution in the right direction.

The circular economy contrasts with the traditional linear economy since it presents a sustainable way both to produce goods and services and to contribute to the development of economies. This paper aims to contribute to a better knowledge of the efficiency of resources productivity, a common indicator to compare how circular economies are, through the estimation of the main determinants for the circular economy in Europe. A systematic analysis and comparison of the performance of all the European Union countries was performed to get further insight into their root causes and to help designing future policies towards a more circular European Union economy. With this purpose, a set of determinant factors for a circular economy in Europe were analysed, under the period between 2000 and 2016. A cluster analysis was applied and complemented with three econometric estimation methods: panel unit root tests, panel cointegration tests and vector autoregression model. The main findings allowed to cluster European countries into three different groups according to the growth rate of their resources productivity and to explain them according to the selected exploratory factors. Special efforts were made to explain the highest productivity growth group, as a way to find relevant drivers towards sustainable productivity growths.

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.

Artículos en revistas Combining large penetration of wind and solar generation with Plug-in Electric Vehicles (PEVs) seems a promising solution for energy cost saving and emissions reduction. PEVs connected to the grid with smart charging strategies can be an effective way to integrate non-dispatchable renewable generation, smoothing the load curve, contributing to the system stability by providing regulation services, and moving unhealthy emissions away from city centers. This paper analyzes the combined penetration of PEVs, and wind and solar generation using a Unit Commitment model for the Spanish power system, providing some insight on how the penetration of these technologies affects relevant variables such as energy and reserve, thermal plants behavior (such as starts-up and shut-downs, technological energy share, generation costs or emissions) and systems costs. Results show that PEV increase total demand, but its optimal charging smooths the net demand (to be supplied by thermal units) and the final electricity prices. In addition, solar generation penetration leads to a larger net demand with more variability but with lower production costs than wind generation penetration, due to their different hourly profiles. Finally neither solar nor wind generation penetrations are totally profitable for the system with the assumptions made, since their investments costs do not compensate the production cost decrement, but grid parity is almost reached for both technologies. info:eu-repo/semantics/publishedVersion