Maximal daily social welfare through demand side management in the day-ahead electricity market

\u3cp\u3eThe day-ahead electricity market is composed of demand and supply orders. Once market participants have sent their orders, the market clearing algorithm decides the accepted and rejected orders, according to the principle of social welfare maximization. This paper proposes the maximization of daily social welfare through demand side management (DSM), modeled as flexible demand orders per time interval. For that purpose, a linear programming model is implemented whose objective function maximizes the daily social welfare following the balance and the demand side management constraints. A test case over 24 hours shows three strategies through a new price order. These strategies are as follows: the new price order is equal to, higher or lower than the price of the original demand order. Some conclusions are also drawn with respect to the social welfare and the consequences for the suppliers.\u3c/p\u3

Portfolio decision of short-term electricity forecasted prices through stochastic programming

\u3cp\u3eDeregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn.\u3c/p\u3

Optimal single wind hydro-pump storage bidding in day-ahead markets including bilateral contracts

\u3cp\u3eThe present evolution of fuel prices together with the reduction of premiums for renewable energies make it of vital importance to improve renewable production management. This paper proposes a model of a single renewable power producer to compete more efficiently against other generators. The single unit is composed of a wind power producer and a hydro-pump storage power producer. The synergies between both renewable producers make relevant the possibility of mitigating wind power uncertainty, and due to this, the imbalances of the wind power producer will be reduced. The reduction of wind imbalances can come from deviating part of the excess of wind generation through a physical connection toward the pumping system or by increasing hydro generation to mitigate the lack of wind generation. To evaluate the problem, stochastic mixed integer linear programming is proposed to address the problem of selling the energy from the single renewable unit through a bilateral contract and in the day-ahead market, as a new contribution to earlier studies. Furthermore, a balancing market is considered to penalize the imbalances. The decision is made to maximize the profit, considering risk-hedging through the Conditional Value at Risk. The model is tested and analyzed for a case study and relevant conclusions are presented.\u3c/p\u3

Robust dynamic transmission and renewable generation expansion planning:walking towards sustainable systems

\u3cp\u3eRecent breakthroughs in Dynamic Transmission Network Expansion Planning (DTNEP) have demonstrated that the use of robust optimization, while maintaining the full temporal dynamic complexity of the problem, renders the capacity expansion planning problem considering uncertainties computationally tractable for real systems. In this paper an adaptive robust formulation is proposed that considers, simultaneously: (i) a year-by-year integrated representation of uncertainties and investment decisions, (ii) the capacity expansion lines have and (iii) the construction and/or dismantling of renewable and conventional generation facilities as well. The Dynamic Transmission Network and Renewable Generation Expansion Planning (DTNRGEP) problem is formulated as a three-level adaptive robust optimization problem. The first level minimizes the investment costs for the transmission network and generation expansion planning, the second level maximizes the costs of operating the system with respect to uncertain parameters, while the third level minimizes those operational costs with respect to operational decisions. The method is tested on two cases: (i) an illustrative example based on the Garver IEEE system and (ii) a case study using the IEEE 118-bus system. Numerical results from these examples demonstrate that the proposed model enables optimal decisions to be made in order to reach a sustainable power system, while overcoming problem size limitations and computational intractability for realistic cases.\u3c/p\u3

Modeling the impact of a wind power producer as a price-maker

\u3cp\u3eWind energy is present in many countries throughout the world. The main types of wind sales in electricity markets are via regulated tariffs or pool-based markets. Production companies choose cost-effective options for selling wind energy, and some markets, like the Irish electricity market, use regulated tariffs to remunerate wind production. This paper aims to provide some answers to explain what effect wind offers may have in an electricity market if wind power producers participated in the day-ahead market without receiving any premium or aid. A price-maker optimization model is used to detect its effect on prices. The model encompasses energy offers by other technologies using residual demand curves and detailed modeling of wind imbalances. It is observed that wind acting as price-maker reduces electricity prices and the imbalance penalties help the system operator to reduce imbalances. A realistic case study using data from the Irish electricity market illustrates the methodology used comparing the effect of imbalance penalties in the models in terms of profit and total imbalance of the system.\u3c/p\u3

Impacts of stochastic wind power and storage participation on economic dispatch in distribution systems

\u3cp\u3eEvaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system.\u3c/p\u3

Optimal bidding of a group of wind farms in day-ahead markets through an external agent

\u3cp\u3eIn deregulated electricity markets, producers offer their energy to the day-ahead market. As the subsidies for renewable producers are becoming lower and lower, they have to adapt to market prices. This paper models the energy trading in the day-ahead market for wind power producers. Different strategies are proposed for this purpose: 1) several wind farms offering their energy separately to the day-ahead market; 2) the same strategy as in 1) but compensating the imbalance among different wind farms; and 3) a joint offer involving several wind farms through an external agent in order to minimize the imbalances between the offer and the final power generation. The strategies are modeled with stochastic mixed integer linear programming and Conditional Value at Risk is used to consider the risk assessment. The expected profit including risk aversion is maximized for each wind power producer and for the set of wind power producers in the case of a joint offer. A comparison of the different cases is described in detail in a case study and relevant conclusions are provided.\u3c/p\u3

Optimal wind reversible hydro offering strategies for midterm planning

\u3cp\u3eA coordinated strategy between wind and reversible hydro units for the midterm planning that reduces the imbalance of wind power and improves system efficiency is proposed. A stochastic mixed integer linear model is used, which maximizes the joint profit of wind and hydro units, where conditional value at risk (CVaR) is used for model risk. The offering strategies studied are 1)separate wind and hydro pumping offer, where the units work separately without a physical connection and 2)a single wind and hydro pumping offer with a physical connection between them to store wind energy for future use. The effects of a coordinated wind-hydro strategy for midterm planning are analyzed, considering CVaR and the future water value. The future water value in the reservoirs is analyzed hourly for a period of 1 week and 2 months, in two realistic case studies.\u3c/p\u3

Short-term trading for a concentrating solar power producer in electricity markets

\u3cp\u3eConcentrating solar power (CSP) plants with thermal energy storage (TES) are emerging renewable technologies with the advantage that TES decreases the uncertainty in the generation of CSP plants. This study introduces a stochastic mixed integer linear programming model, where the objective function is the maximization of the expected profit that can be obtained by selling the energy generated by the CSP plant in the day-ahead electricity market. The proposed model considers three main blocks of constraints, namely, renewable generator constraints, TES constraints, and electricity market constraints. The last category of constraints considers the penalties incurred due to positive or negative imbalances in the balancing market. A case study using data from the Spanish electricity market is introduced, described and analyzed in terms of trading of the CSP plant generation. The conclusions highlight the influence of TES capacity on the energy trading profile, the expected profits and the volatility (risk) in the trading decisions.\u3c/p\u3

Optimal coordinated wind-photovoltaic bidding in electricity markets

The high penetration of wind and photovoltaic power in electricity markets will represent a major challenge in the forthcoming years. The main problem of both technologies is the high uncertainty in their production and their dependence on environmental conditions. The coordination between wind and photovoltaic power aims to lower imbalances, reducing their associated penalties. This paper describes two strategies: i) separate wind and photovoltaic strategy and ii) single wind-photovoltaic strategy. The strategies proposed are solved through stochastic mixed integer linear programming. The expected profits are maximized and they are obtained by selling the energy in the day-ahead market. The imbalances are penalized in the balancing market as well. The model is tested for a week, 168 hours, and the data used come from the Spanish electricity market. The results of the case study are discussed, comparing both strategies. Following the discussion, the most important conclusions are presented