Pedro Sanchez-Martin

Security-Constrained Optimal Power and Natural-Gas Flow

Continuous liberalization and interconnection of energy markets worldwide has raised concerns about the inherent interdependency between primary energy supply and electric systems. With the growing interaction among energy carriers, limitations on the fuel delivery are becoming increasingly relevant to the operation of power systems. This paper contributes with a novel formulation of a mixed-integer linear programing (MILP) security-constrained optimal power and gas flow. To this end, an iterative methodology, based on development of linear sensitivity factors, determines the stabilized post-contingency condition of the integrated network. The proposed model allows system operators not only to perform security analysis but also to adjust in advance state variables of the integrated system optimally and fast, so that n-1 contingencies do not result in violations. Case studies integrate the IEEE 24-bus system and a modified Belgian high-calorific gas network for analyzing the performance of the formulation and solution methodology.

Probabilistic midterm transmission planning in a liberalized market

This paper shows a midterm transmission planning methodology for liberalized electricity markets. This methodology evaluates expansions and reinforcements using a transmission adequacy linear programming model. This type of modeling solves efficiently, taking into account power exchange deviations, n-1 network preventive adequacy level, and nonsupply demand. Statistical results are obtained sampling power exchange scenarios and computing transmission investment sensitivities. After each sample of generation and consumption bidding and generator and circuit failures, means, ranges, and confidence intervals of transmission investment sensitivities are updated. These sensitivities are computed using dual variables and reduced costs of the transmission adequacy model. This statistical sensitivity information and additional information are evaluated jointly using multicriteria decision theory. An extended Garvers six-bus and the Spanish system cases are analyzed.

Direct Load Control Decision Model for Aggregated EV Charging Points

This paper details a decision model to implement direct load control (DLC) on battery charging processes at electric vehicle charging points located at parking areas. The programming model combines optimally three types of energy management decisions: grid-to-vehicle charges, vehicle-to-grid discharges, and novel vehicle-to-vehicle energy exchanges. The objective function maximizes the net energy supplied to batteries minimizing simultaneously the global energy cost. A 50-plug-in vehicle park case is analyzed for three possible mobility patterns: household, commercial and mixed. Outputs from the DLC model are compared with the ones using a dumb charging policy from the service quality and economic points of view. Finally, a sensitivity analysis has been done to evaluate the economic impact of the Depth of Discharge condition to preserve battery lifecycle of electric vehicles.

Integrated Power and Natural Gas Model for Energy Adequacy in Short-Term Operation

The significant growth in gas-fired units worldwide has increased the grade of interdependency between power and natural gas networks. Since these units are usually required to ramp up during the peak and backup intermittent renewable generation and contingencies, the power system tends to demand more flexibility and reliability from the gas system. This paper contributes with a novel mixed-integer linear programming (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility. As a result, the model accounts for the gas adequacy needed to assure the power system reliability in the short term. The robustness of the MILP formulation allows guaranteeing global optimality within predefined tolerances. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network for validating the formulation.

Optimal electric vehicles consumption management at parking garages

This paper is focused on the electric power management required on garages where plug-in vehicles are located. As the Electric Vehicle technology becomes more efficient and sophisticated, parking infrastructures should be analyzed as new potential high concentrated electric consumption nodes. Simulation techniques have been implemented to analyze possible benefits of implementing a consumption management control on these types of infrastructures. Power capacity requirements and savings on energy consumption, capacity costs and non supplied energy penalties are some outputs extracted from a 50 plug-in vehicles case study.

Stochastic Programming Applied to EV Charging Points for Energy and Reserve Service Markets

A more realistic management of electric vehicle (EV) charging points requires to cope with stochastic behavior on vehicle staying patterns. This paper presents a stochastic programming model to achieve optimal management taking into account price variation in day-ahead and intraday electricity markets, together with regulating reserve margins. In this model, first-stage decisions determine day-ahead energy purchases and sales and the upward and downward reserve margins committed. Second-stage decisions correspond to intraday markets and deal with reserve requirements and several possible scenarios for vehicle staying pattern. The design of the objective function prioritizes supplying energy to EV batteries while minimizing the net expected energy cost at the EV charging point. A case study describing a parking for 50 EVs is analyzed. The case includes household, commercial and mixed EV staying patterns with several intraday arrival and departure scenarios. Pure and hybrid EVs are included, taking into account their respective energy characteristics. Sensitivity analysis is used to show the potential energy cost savings and the impact of different non-supply penalizations. The case study considers several vehicle staying patterns, energy price profiles and discharge allowances. The model achieves energy cost reductions between 1% and 15% depending on the specific case. A model validation by simulation has been done.

Stochastic contingency analysis for the unit commitment with natural gas constraints

The continuing liberalization and interconnection of energy markets worldwide has raised concerns about the inherent interdependency between primary energy supply and electric systems. With the growing interaction among energy carriers, limitations on the fuel delivery are increasingly relevant to the operation of power systems. One such issue is the impact of fuel supply in the Unit Commitment (UC). The contribution of this paper focuses on the development of a MIP UC that integrates the power and natural gas networks for the analysis of infrastructure outages. The problem is formulated as a two-stage stochastic optimization problem in which the first stage optimizes commitment and production decisions and the second computes deviations in levels of production and energy flows according to possible network contingencies. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network to compute the effect of network uncertainty in the UC, showing the importance of interactions among energy systems.

Dynamic Ramping Model Including Intraperiod Ramp-Rate Changes in Unit Commitment

The growing increase of renewable generation worldwide is posing new challenges for a secure, reliable, and economic operation of power systems. In order to face the uncertain and intermittent production of renewable sources, operating reserves must be allocated efficiently and accurately. Nowadays, these reserves are mainly assigned to thermal units, especially gas-fired generators, due to their operation flexibility and fast response. However, the ramping capabilities of these units define the grade of flexibility offered to the system operation. In practical applications, ramping limits are dynamic, i.e., they are a function of the units generating output. Omitting this feature leads to suboptimal or even infeasible reserve allocations, thus increasing not only operating reserve requirements but also transactions in real-time balancing markets needed to back up deviations of renewable generation. This paper contributes with a mixed-integer linear programming model for units’ dynamic ramping allowing intraperiod changes in the unit commitment problem. As a result, operating reserves are better allocated and the units’ flexibility is managed more efficiently than traditional ramping models found in the literature. Different case studies illustrate the functioning and benefits of the proposed formulation.

Security Constrained Unit Commitment Using Line Outage Distribution Factors

Security-constrained unit commitment (SCUC) problem is one of the necessary tools for system operators to make operational planning and real-time operation. The internalization of transmission network and security constraints (e.g., N-1 criterion) could lead to different decisions in the generation dispatch. However, the computational burden of this problem is challenging mainly due to its inherent large problem size. Therefore, this paper proposes an N-1 security constrained formulation based on the line outage distribution factors (LODF) instead of the one based on injection sensitivity factors (ISF). This formulation is at the same time more compact than analogous formulations for contingency constraints; hence, it presents a lower computational burden. The computational efficiency of the proposed formulation is shown by solving the SCUC of the IEEE 118 bus system with LODF and ISF. Additionally, an iterative methodology for filtering the active N-1 congestion constraints is detailed, and its implementation for large-scale systems is described. The results show that the proposed filter reduces the computational time by approximately 70% in comparison to the complete formulation of N-1 constraints in SCUC.

Day ahead and intraday stochastic decision model for EV charging points

The paper details a stochastic programming model to optimize decisions on battery charging and Grid ancillary services at Electric Vehicle (EV) Charging Points. First and second decision stages deal with stochasticity on EV staying pattern. Day-ahead and Intraday electric markets are included respectively as first and second stages for energy and reserves prices. Day-Ahead decisions - first stage - are hourly energy purchases and sales and, also upward-downward reserve sales. Intraday decisions - second stage - deals with different scenarios of vehicle staying and supplying reserves. The global objective function prioritizes supplying energy to EV batteries and at the same time minimizes the net expected energy cost at the EV Charging Point taking into account energy and reserve markets. A 50 plug-in vehicle parking is analyzed with household, commercial and mixed staying patterns and several stochastic arrival-departure scenarios. Output comparison is shown between Day-Ahead and Intraday decisions and resulting average cost per kWh.