Hydrogen produced without carbon emissions could be a useful fuel as nations look to decarbonize their electricity, transport, and industry sectors. Using the iodine−sulfur (IS) cycle coupled with a nuclear heat source is one method for producing hydrogen without the use of fossil fuels. An economic dispatch model was developed for a nuclear-driven IS system to determine hydrogen sale prices that would make such a system profitable. The system studied is the HTTR-GT/H2, a design for power and hydrogen cogeneration at the Japan Atomic Energy Agency’s High Temperature Engineering Test Reactor. This study focuses on the development of the economic model and the role that input data plays in the final calculated values. Using a historical price duration curve shows that the levelized cost of hydrogen (LCOH) or breakeven sale price of hydrogen would need to be 98.1 JPY/m3 or greater. Synthetic time histories were also used and found the LCOH to be 67.5 JPY/m3. The price duration input was f... [more]

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get... [more]

The anticipated electrification of the transport sector may lead to significant increase in the future peak electricity demand, resulting in potential violations of network constraints. As a result, a considerable amount of network reinforcement may be required in order to ensure that the expected additional demand from electric vehicles that are to be connected will be safely accommodated. In this paper we present the Backwards Induction Framework (BIF), which we use for identifying the optimal investment decisions, for calculating the option value of smart charging of EV and the cost of stranded assets; these concepts are crystallized through illustrative case studies. Sensitivity analyses depict how the option value of smart charging and the optimal solution are affected by key factors such as the social cost associated with not accommodating the full EV capacity, the flexibility of smart charging, and the scenario probabilities. Moreover, the BIF is compared with the Stochastic Opt... [more]

Mineral and metallurgical processing are crucial within the mineral value chain. These processes involve several stages wherein comminution is arguably the most important due to its high energy consumption, and its impact on subsequent extractive processes. Several geological properties of the orebody impact the efficiency of mineral processing and extractive metallurgy; scholars have therefore proposed to deal with the uncertain ore feed in terms of grades and rock types, incorporating operational modes that represent different plant configurations that provide coordinated system-wide responses. Even though these studies offer insights into how mine planning impacts the ore fed into the plant, the simultaneous optimization of mine plan and metallurgical plant design has been limited by the existing stochastic mine planning algorithms, which have only limited support for detailing operational modes. The present work offers to fill this gap for open-pit mines through a computationally e... [more]

The continuous increase in generation from renewable energy sources, marked by correlated forecast uncertainties, requires specific methodologies to support power system operators in security management. This paper proposes a probabilistic preventive control to ensure N-1 security in presence of correlated uncertainties of renewable sources and loads. By adopting a decoupled linear formulation of the AC load flow equations, the preventive control is decomposed into two subsequent linear programming problems, the former concerning the active power and the latter the voltage/reactive power-related issues. In particular, in the active control problem, the algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method, and Cornish−Fisher expansion to model the forecast uncertainties and characterize the chance constraints in the problem. The goal is to find the optimal phase shifting transformer tap setting, conventional generation redispatching, and renewable curta... [more]

This work discusses what should be the desirable path and correct tools for the optimal re-design and operation of processes in the Industry 4.0 framework, as illustrated in a challenging case study corresponding to a complex network of evaporation plants in a viscose-fiber factory. The goal is to integrate optimal design, to improve the existing cooling systems, together with the optimal operation of the whole network, balancing the initial investment with the potentially achievable savings. A rigorous mathematical model for such optimization purpose has been built. The model explicitly considers different structural alternatives as a superstructure for the incorporation of new equipment into the network. The uncertainty associated to future operating conditions is also considered by using a two-stage stochastic formulation. Furthermore, the model is also the base from which a deterministic real-time optimization (RTO) builds upon to support the daily management of the future network... [more]

This work proposes a structural enhancement and a new technique to design the loop filter (LF) of a third-order phase-locked loop (PLL) to enhance the PLL dynamic performance under abnormal grid conditions. The proposed PLL combines a moving average filter (MAF) and an arbitrarily delayed signal cancelation (ADSC) for structural enhancement to achieve DC-offset rejection and harmonics elimination. The window length of the MAF is selected to be one-sixth of the fundamental grid period to remove non-triple odd harmonics and speed up the PLL dynamic response. The triple harmonics are eliminated, adopting the line-to-line voltage concept, while the ADSC operator rejects the DC offset. The LF design is based on a modified third-order polynomial tuned using stochastic optimization to minimize the settling time of the frequency deviation, offering better dynamic performance over the symmetrical optimum method (SOM) and achieving synchronization within one grid cycle. The PLL mathematical mode... [more]

The application of integrated energy systems is significant for realizing the comprehensive utilization of various energy sources and improving the utilization rate of renewable energy. At present, the optimal operation of integrated energy systems is a research hotspot. However, shortcomings remain in the stochastic optimization operation and the scenario generation method. This paper proposes a stochastic optimization operation model of an integrated energy microgrid based on an advanced multi-scenario generation method. First, this paper establishes the time-divided probability distribution model of the forecasting error of the uncertain factors, such as photovoltaic (PV) power and load, which provide the basis for generating scenarios. Moreover, the covariance matrix is used to calculate the time correlation of the time-divided probabilistic distributed models, and the parameters of the covariance matrix are optimized. Second, based on multiple typical scenarios, the stochastic opt... [more]

A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders dec... [more]

Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization mode... [more]