Jesus M. Latorre

Demand Response in an Isolated System With High Wind Integration

Growing load factors in winter and summer peaks are a serious problem faced by the Spanish electric energy system. This has led to the extensive use of peak load plants and thus to higher costs for the whole system. Wind energy represents a strongly increasing percentage of overall electricity production, but wind normally does not follow the typical demand profile. As generation flexibility is limited due to technical restrictions, and in absence of large energy storages, the other side of the equilibrium generation-demand has to react. Demand side management measures intend to adapt the demand profile to the situation in the system. In this paper, the operation of an electric system with high wind penetration is modeled by means of a unit commitment problem. Demand shifting and peak shaving are considered in this operation problem. Demand shifting is modeled in two different ways. Firstly, the system operator controls the shift of demand; secondly, each consumer decides its reaction to prices depending on its elasticity. The model is applied to the isolated power system of Gran Canaria. The impact of an increased installed wind capacity on operation and the cost savings resulting from the introduction of responsive demand are assessed. Furthermore, results from the different implemented demand response options are compared.

Tight and Compact MILP Formulation for the Thermal Unit Commitment Problem

This paper presents a mixed-integer linear programming (MILP) reformulation of the thermal unit commitment (UC) problem. The proposed formulation is simultaneously tight and compact. The tighter characteristic reduces the search space and the more compact characteristic increases the searching speed with which solvers explore that reduced space. Therefore, as a natural consequence, the proposed formulation significantly reduces the computational burden in comparison with analogous MILP-based UC formulations. We provide computational results comparing the proposed formulation with two others which have been recognized as computationally efficient in the literature. The experiments were carried out on 40 different power system mixes and sizes, running from 28 to 1870 generating units.

Tight and Compact MILP Formulation of Start-Up and Shut-Down Ramping in Unit Commitment

This paper presents a mixed-integer linear programming (MILP) formulation of start-up (SU) and shut-down (SD) power trajectories of thermal units. Multiple SU power-trajectories and costs are modeled according to how long the unit has been offline. The proposed formulation significantly reduces the computational burden in comparison with others commonly found in the literature. This is because the formulation is 1) tighter, i.e., the relaxed solution is nearer to the optimal integer solution; and 2) more compact, i.e., it needs fewer constraints, variables and nonzero elements in the constraint matrix. For illustration, the self-unit commitment problem faced by a thermal unit is employed. We provide computational results comparing the proposed formulation with others found in the literature.

Stochastic dual dynamic programming applied to nonconvex hydrothermal models

In this paper we apply stochastic dual dynamic programming decomposition to a nonconvex multistage stochastic hydrothermal model where the nonlinear water head effects on production and the nonlinear dependence between the reservoir head and the reservoir volume are modeled. The nonconvex constraints that represent the production function of a hydro plant are approximated by McCormick envelopes. These constraints are split into smaller regions and the McCormick envelopes are used for each region. We use binary variables for this disjunctive programming approach and solve the problem with a decomposition method. We resort to a variant of the L-shaped method for solving the MIP subproblem with binary variables at any stage inside the stochastic dual dynamic programming algorithm. A realistic large-scale case study is presented.

Demand response and its sensitivity to participation rates and elasticities

Activating the demand-side of the electric system is a comeback of an old idea. What decades ago did not work out due to the lack of proper technology, today raises hopes to meliorate some of the most problematic situations in electric system operation such as ever higher peak demands and high wind generation during low demand periods. Smart grid infrastructures are currently implemented in many countries. This communication and control infrastructure allows consumers to receive information on system conditions, for example in the form of price signals, and thus to react to these and reduce, increase or shift their electricity consumption. This paper presents the modelling of demand shifting with two Demand Response mechanisms, Direct Load Control and Dynamic pricing. The outcome of both mechanisms depends, to a great extent, on two parameters: the maximum share of load which consumers are able and willing to shift and the elasticities used to express consumers level of responsiveness in the dynamic pricing mechanism. An analysis of the sensitivity of the impact of Demand Response is carried out by varying these two parameters over a large range. Results regarding demand participation shares, cost savings, demand variation patterns and used generation technologies are compared for the different sensitivity cases. We find that cost saving increases are not proportional to increments in the maximum share of participating demand and in responsiveness to prices.

Smart charging profiles for electric vehicles

Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the effects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these effects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in variable costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an “intelligent” charging strategy. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using day-ahead management systems instead of pre-set profiles. This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on variable costs, emissions and renewable energy sources integration will be obtained using an operation planning model. The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies. The results show the benefits of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. Moreover, the benefits of using a day-ahead management system are also evaluated, resulting in a small reduction of system variable cost compared to the use of pre-defined charging profiles.

Analysis of stochastic problem decomposition algorithms in computational grids

Stochastic programming usually represents uncertainty discretely by means of a scenario tree. This representation leads to an exponential growth of the size of stochastic mathematical problems when better accuracy is needed. Trying to solve the problem as a whole, considering all scenarios together, yields to huge memory requirements that surpass the capabilities of current computers. Thus, decomposition algorithms are employed to divide the problem into several smaller subproblems and to coordinate their solution in order to obtain the global optimum. This paper analyzes several decomposition strategies based on the classical Benders decomposition algorithm, and applies them in the emerging computational grid environments. Most decomposition algorithms are not able to take full advantage of all the computing power available in a grid system because of unavoidable dependencies inherent to the algorithms. However, a special decomposition method presented in this paper aims at reducing dependency among subproblems, to the point where all the subproblems can be sent simultaneously to the grid. All algorithms have been tested in a grid system, measuring execution times required to solve standard optimization problems and a real-size hydrothermal coordination problem. Numerical results are shown to confirm that this new method outperforms the classical ones when used in grid computing environments.

Coordinated Hydropower Plant Simulation for Multireservoir Systems

AbstractHydroreservoirs usually serve two main purposes: hydropower production and water consumption. The great flexibility, low operating costs, and low carbon impact of hydroturbines turns them into a desirable technology in the generator mix of power systems. In addition, sustainability and environmental concerns support their use in current power systems, along with other renewable energy sources like wind and solar energy. However, the stochastic nature of river inflows hinders their long-term use and hints at the need to use planning tools. Furthermore, it also requires the use of planning tools in order to balance present and future requirements. This work presents a simulation tool that is employed at Iberdrola to help in the preparation of medium-term hydroelectric production schedules. The main objective of the simulation is to follow the production guidelines given by a long-term hydrothermal problem, while avoiding spillages and failures to fulfill water release agreements. In order to achieve...

A Decision Support Model for Weekly Operation of Hydrothermal Systems by Stochastic Nonlinear Optimization

This chapter formulates and solves an optimal resource allocation problem of thermal and hydropower plants with multiple basins and multiple connected reservoirs. The stochastic factors of the problem are here represented by natural hydro inflows. A multivariate scenario tree is in this case obtained taking into account the stochastic inputs and their spatial and temporal dependencies. The hydropower plant efficiency depends on its water head and the reservoir volume depends nonlinearly on the headwater elevation, leading to a large-scale stochastic nonlinear optimization problem, whose formulation and solution are detailed in the case study. An analysis of exhaustive alternatives of computer implementation is also discussed.

The Role of Flexible Demands in Smart Energy Systems

The demand side of the electricity system holds a flexibility resource which has been ignored for a long time. With the presence of smart grids and facing challenges such as the massive integration of renewable energies into the system, demand side measures become viable and indispensable. This chapter will give a brief introduction about the concept of demand response. It will give an overview on demand response mechanisms, their objectives and potentials. Furthermore, an overview about various flexible demands in households, commerce and industries is given.