Juan M. Lujano-Rojas

Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

A Probabilistic Approach to Solve Economic Dispatch Problem in Systems with Intermittent Power Sources

As a crucial factor in global energy consumption, environmental problems related to the greenhouse gas emissions and high oil prices have motivated the growth and incorporation of alternative sources of energy into power systems. However, one of the most important facets of such problems is their intermittent nature, which means that the operation and management of a power system is a difficult task. In this paper, a probabilistic approach to solving the economic dispatch (ED) problem under conditions of uncertainty is presented. The proposed methodology allows for obtaining the probability distribution function (PDF) of all generation units, the energy not supplied (ENS) and the total generation cost. A case study based on an insular power system is analyzed, with reference to the important relationship that exists between the PDF of net load demand and the PDF of ENS, power production and total generation cost.

A Qualitative Evaluation of Operational Conditions in PV/Wind/Battery Systems

Hybrid power systems (HPS) play an important role in places located far from electric grids. The sizing of these systems is difficult to determine due to the variable nature of renewable energy resources and the complex behaviour of their components. The battery bank has a significant impact on the performance of hybrid systems due to their complex behaviour and high investment costs. This situation has motivated the development of different approaches to improve the mathematical model of the lead acid battery. However, the accuracy of some of these approaches is still unclear. In this paper, using qualitative information about determined operating conditions, a small capacity hybrid power system installed in Zaragoza is analyzed, concluding that this information could be useful to HPS designers.

Impacts of Demand Response on oligopolistic behavior of electricity market players in the day-ahead energy market

This paper investigates the effects of Demand Response Programs (DRPs) on the behavior of electricity market players in the day-ahead energy market. To this end, an electricity market environment is proposed based on the multi-agent systems in order to model the strategic self-scheduling of each market player as an individual agent. In such oligopolistic environment, market interactions are considered by using a game theoretic model and the market transactions are cleared by means of a security constrained unit commitment problem. Different types of DRPs are also considered consisting of Time Of Use (TOU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), and Emergency Demand Response Program (EDRP). The proposed model is applied on a modified IEEE six-bus test system. The numerical results indicate that different types of DRPs differently affect the oligopolistic behavior of market players that should be studied by the system operators before their implementation.

Including forecasting error of renewable generation on the optimal load dispatch

In this paper a probabilistic model to solve the economic dispatch (ED) problem considering the uncertainty introduced by power sources, such as wind and solar, is presented. Assuming the forecasting error to be modeled by a beta probability distribution function (PDF), the proposed methodology presented in this paper allows the incorporation of this PDF in the optimization model, obtaining the PDF of power production of thermal and renewable generators, energy not supplied, excess of electricity, and generation cost. The results obtained from the proposed methodology are compared with those obtained from Monte Carlo Simulation (MCS) approach, observing a good agreement.

Probabilistic approach to describe greenhouse gas emissions and their impact in the operation of insular power systems

Environmental problems related to the conventional generators have motivated governmental policies all over the world in order to incorporate alternative power sources to reduce greenhouse gas (GHG) emissions and fossil-fuel consumption. On the one hand, wind power generation can increase GHG emissions of the others conventional generators connected to the system. On the other hand, wind energy is characterized by its variability that imposes challenges in the operation of the power system. In order to integrate GHG emissions estimation and the uncertainty related to the wind power generation, a probabilistic point of view is presented in this paper to estimate the probability distribution function (PDF) of the GHG emissions of a typical insular power system. The PDF of power production of each conventional generator is calculated, and then the expected value of the total GHG emissions is estimated.

Probability theory-based economic dispatch model for insular power systems

The main problem in integration of renewable power sources to the electricity grid is the uncertainty introduced by the power forecasting process in the optimal scheduling problem, which can considerably increase the generation cost. This problem has been widely analyzed using scenario generation/reduction methodologies. However, the consideration of a reduced number of scenarios can limit the capabilities of these methodologies. As an alternative, in this manuscript the dynamic economic dispatch problem has been solved by estimating the probability density function of energy surplus, the energy not supplied and the power production considering the forecasting error and system reliability. The incorporation of the system reliability and the forecasting error as probability distribution functions can avoid the use of scenario generation and reduction processes, which are time consuming tasks. The proposed model was illustrated by analyzing a typical insular power system under different conditions of load and uncertainty, concluding that the hardware failure can introduce a relevant increment in the generation costs, due to their relationship with the value of lost load. Moreover, the scalability of the proposed model was studied by analyzing several power systems between 10 and 150 units, which have been solved in an acceptable computational time.

Modeling the Multiobjective Optimization of Electricity Consumption for Residential Consumers

This paper presents a mathematical model to simultaneously optimize the cost of electricity and the satisfaction of a residential consumer using the communication infrastructure of a smart grid. For this task the concept of Pareto optimality has been used. It is possible to consider the satisfaction of the consumer as an independent objective to be maximized, and simultaneously, to minimize the cost of the electrical bill. In future works a multiobjective evolutionary algorithm will be applied along with the mathematical model presented in this paper.

Optimal scheduling strategy in insular grids considering significant share of renewables

Due to the uncertainty and stochastic behavior of wind and photovoltaic production introduced in conventional power systems, the correct overall management considering all the technical and economic constraints is faced with more challenges. To address also the specificities of insular power systems, several strategies have been proposed in last years, including energy storage systems with the aim of increasing system flexibility. Accurate forecasting tools may also help to reduce overall uncertainty. Other scheduling tools based on probabilistic, heuristic and stochastic programming have also been considered. In this work, a new scheduling strategy is proposed considering the integration of wind production in an insular power system. To this end, some arbitrarily chosen scenarios from wind production are introduced in the scheduling process, and a comparative study is carried out, with and without renewable production, providing an acceptable computational time.

Optimizing daily operation of battery energy storage systems under real-time pricing schemes

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.