Jose M. Arroyo

Bio: Jose M. Arroyo is an academic researcher from University of Castilla–La Mancha. The author has contributed to research in topics: Linear programming & Robust optimization. The author has an hindex of 41, co-authored 99 publications receiving 8431 citations.

A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem

Abstract: This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions

Optimal response of a thermal unit to an electricity spot market

Abstract: This paper addresses the optimal response of a thermal unit to an electricity spot market. The objective is to maximize the unit profit from selling both energy and spinning reserve in the spot market. The paper proposes a 0/1 mixed-integer linear programming approach that allows a rigorous modeling of (i) nonconvex and nondifferentiable operating costs, (ii) exponential start-up costs, (iii) available spinning reserve taking into account ramp rate restrictions, and (iv) minimum up and down time constraints. This approach overcomes the modeling limitations of dynamic programming approaches and is computationally efficient. Results from realistic case studies are reported.

Transmission loss allocation: a comparison of different practical algorithms

Abstract: A pool-operated electricity market based on hourly auctions usually neglects network constraints and network losses while applying its market-clearing mechanism. This mechanism determines the accepted and nonaccepted energy bids as well as the hourly market-clearing prices. As a result, ex post procedures are needed to resolve network congestions and to allocate transmission losses to generators and demands. This paper focuses on transmission loss allocation procedures and provides a detailed comparison of four alternative algorithms: (1) pro rata (PR); (2) marginal allocation; (3) unsubsidized marginal allocation; and (4) proportional sharing. A case study based on the IEEE RTS is provided. Different load scenarios covering a whole year are analyzed. Finally, conclusions and recommendations are stated.

Scheduling and Pricing of Coupled Energy and Primary, Secondary, and Tertiary Reserves

Abstract: Current practice in some electricity markets is to schedule energy and various reserve types sequentially, first clearing the energy market, followed by the reserves needed. Since distinct reserve services can in fact be strongly coupled, and the heuristics required to bridge the various sequential markets can ultimately lead to loss of social welfare, simultaneous energy/reserves market-clearing procedures have been proposed and are in use. However, they generally schedule reserve services subject to exogenous rules and parameters that do not relate to actual operating conditions. The weaknesses of the current approaches warrant the investigation of alternatives. In that regard, we present a different methodology to the simultaneous market clearing of energy and reserve services. This approach avoids the pitfalls of the sequential procedures, while at the same time its basis for scheduling reserve services does no longer rely on some rules of thumb. The salient feature of the proposed approach is that, under marginal pricing, it yields a single price for all reserve types scheduled at a bus, unlike the current approaches. We show that this common price is given by the nodal marginal cost of security. We present two specific implementations of a simultaneous security-constrained market-clearing procedure, one deterministic and one probabilistic. An example of joint market clearing of energy with reserves required for primary and tertiary regulation illustrates how their strong coupling affects their schedule and prices.

Self-scheduling of a hydro producer in a pool-based electricity market

Abstract: This paper addresses the self-scheduling of a hydro generating company in a pool-based electricity market. This company comprises several cascaded plants along a river basin. The objective is to maximize the profit of the company from selling energy in the day-ahead market. This paper proposes a 0/1 mixed-integer linear programming model to account, in every plant, for the nonlinear and nonconcave three-dimensional (3-D) relationship between the power produced, the water discharged, and the head of the associated reservoir. Additionally, start-up costs due mainly to the wear and tear are considered. Finally, different realistic case studies are analyzed in detail.

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem

Abstract: This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

Abstract: Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

ARIMA models to predict next-day electricity prices

Abstract: Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

A comprehensive review of firefly algorithms

Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.