The purpose of this special issue of IISE Transactions on Operations Engineering and Analytics was to create a repository of cutting edge industrial engineering research with strong methodological focus addressing different aspects of emerging problems in the energy transformation process. An open call for papers in IISE Transactions was announced in August 2016. The eight articles published herein were selected among 39 submitted manuscripts, following the standard, rigorous review procedure of IISE Transactions. The topics can be classified into four categories. The first category includes two articles that study the gas storage and gas network problems (Secomandi; and Cay et al.). The second category includes two articles that study different approaches for optimizing the electricity energy storage (Moazeni and Defourny; and Bhattacharya et al.). The third category includes two articles that study the electricity market design and demand response program (Khadgi and Bai; and Zhou et al.). Finally, the last category includes two articles that study electricity generation scheduling and capacity expansion problems (Zhan and Zheng; and Guan et al.).
More specifically, in the first article, “An improved basket of spread options heuristic for merchant energy storage,” Secomandi proposes an improved version of the original Basket of Spread Options (BSO) heuristic to model merchant energy storage as a portfolio of spread options and spot/forward sales. In this revision, the objective function of the BSO linear program based on exchange option prices and a tunable parameter is modified. The new heuristic has been tested on a set of known natural gas storage instances and the results show that limited optimization of this adjustable quantity leads to modestly improved Rolling BSO (RBSO) policies on average but substantially so when the original RBSO policies perform poorly, which occurs on some fast storage instances. Furthermore, the proposed BSO heuristic is as easy to use in practice as the original BSO heuristic.
In the second article, “Solutions with performance guarantees on tactical decisions for industrial gas network problems,” Cay, Esmaili, Mancilla, Storer, and Zuluaga first study the performance of different approaches presented in the literature to solve both natural gas and industrial gas problems. In these models, physical constraints in a gas pipeline network are included. The reported results include either finding global optimal solutions, or determining the optimality gap between a local optimal solution and a valid lower bound for the problem's objective. Next, the authors propose alternative reformulations for the operational level gas pipeline optimization problem. The final computational results indicate that although the performance of alternative reformulations vary in terms of optimality gap and solution time, in industry-sized instances, significant improvements can be made, as compared to the standard formulation approach.
In the third article, “Optimal control of energy storage under random operation permissions,” Moazeni and Defourny study an interesting energy storage control problem in which operations are permitted only at random times. In this study, the storage operator decides whether to transact at the arrival of each permission with the objective of maximizing the expected cumulated revenue over a finite horizon. Accordingly, the corresponding optimization problem is modeled as a piecewise deterministic Markov decision process. As compared to related research, a nonlinear pricing structure is introduced in this study, which incentivizes small transactions spread out among arrivals. In addition, various properties and sensitivity analysis are explored and numerical studies are executed to justify the theoretical results.
In the fourth article, “Structured storage policies for energy distribution networks,” Bhattacharya, Kharoufeh, and Zeng consider the energy storage dynamic control problem in a two-bus energy distribution network. In the proposed framework, an operator can dynamically adjust the charge/discharge amount of energy to/from energy storage devices in response to random demand, supply and prices. Eventually the problem is formulated as a finite-horizon Markov decision process with the objective of minimizing the expected total discounted costs. The authors further explore the properties of the value function and the optimal policy to reduce the computational time, and extend the results to more general multi-bus network settings.
In the fifth article “A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge,” Khadgi and Bai study dynamic variable pricing for the demand response program. A novel pricing scheme is introduced to reduce load consumption during peak events. In this study, the authors employ the multi-attribute utility function and model predictive control to simulate consumers' behavior in their home energy consumption, and the conditional Markov chain is used to model and predict the system peak. Effectiveness of the proposed pricing scheme is compared with other pricing schemes through simulation based on real-world residential load profiles.
In the article “Backup capacity coordination with renewable energy certificates in a regional electricity market,” Zhou, Liu, and Zhao provide an innovative market design for a regional electricity market to promote renewable generation, considering the random power shortage caused by the intermittent nature of the renewable energy. A coordination scheme between a renewable supplier and a conventional supplier is explored. By offering the conventional supplier renewable energy certificates proportional to the backup capacity committed from the renewable supplier, the system profit can be split between the two suppliers. It is inspiring that under most cases the social welfare under this mechanism is higher than that in the decentralized case.
In the article “A multistage decision-dependent stochastic bi-level programming approach for power generation investment expansion planning,” Zhan and Zheng introduce a bilevel optimization model to address the power generation expansion planning under uncertainty problem. As compared to the related studies in the literature, the authors employ a decision-dependent stochastic programming approach to address the uncertainties. To solve the problem efficiently, the authors have successfully reformulated the proposed bilevel optimization model as a single-level optimization problem, and solved the model by decomposition algorithms. The numerical results on IEEE reliability test systems show the effectiveness of their proposed approach.
In the last article, “Polynomial time algorithms and extended formulations for unit commitment problems,” Guan, Pan, and Zhou derive polynomial-time algorithms for the Unit Commitment (UC) problems which are fundamental for electricity generation scheduling. The authors refine an O(T3) time algorithm for the deterministic UC problem with general convex cost function. Besides, the authors further develop an extended formulation, which can solve the UC as a linear program, by introducing additional decision variables. The authors further refine the algorithm to O(T) time when the cost function is piecewise linear instead of general convex, by exploring the optimality conditions. Finally, this study is extended to the multistage stochastic scenario tree setting.
We hope that the articles in this special issue will help expand the scope and increase the impact of IISE Transactions on broad energy research areas. The manuscripts that were submitted to this issue but were under review at the time of this writing will appear in subsequent issues of IISE Transactions, if accepted.
In closing, we would like to thank the authors and the numerous anonymous referees who contributed to the review process of this issue. We would also like to thank Professor Ronald Askin, Editor in Chief of IISE Transactions until 2017, his successor, Professor Jianjun (Jan) Shi, and the Editor of the Focused Issue on Operations Engineering and Analytics, Professor Sheldon H. Jacobson, for supporting this special issue.
Special Issue Editors