Optimal residential load scheduling under dynamic pricing and demand-side management

Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.

A Prescriptive Machine-Learning Framework to the Price-Setting Newsvendor Problem

The approach to data-driven optimization described in this paper was developed when the authors were part of an IBM project team working with the U.S. Department of Energy, Pacific National Laboratory, and various energy utility partners on an initiative to develop a smart energy distribution infrastructure. Within this broader scope and based on the data collected in some initial controlled experiments, the paper specifically addresses the design and optimization of real-time price incentives to consumers to manage their electricity demand and determine the energy capacity to be provisioned by the utility. This latter problem fits into the well-known price-setting newsvendor problem framework, and our goal was to replace the simplistic methods in the literature by more realistic data-driven methods to take into account the data-collection capabilities and the modeling complexity of real-world applications. Our aspirations for the paper are (1) to introduce data-driven, distribution-free approaches to decision-making problems and (2) to motivate scalable conditional value-at-risk regression-based approaches for these problems.

Grid Parity Analysis of China’s Centralized Photovoltaic Generation under Multiple Uncertainties

The cost of centralized photovoltaic (CPV) power generation has been decreasing rapidly in China. However, the achievement of grid parity is full of uncertainties due to changes in policies and the industry environment. In order to explore the time, price, and external conditions in which grid parity can be achieved, we create the improved grey GM (1, 1) model to estimate the installed capacity over the next 10 years, and apply a learning curve to predict the cost of CPV generation. In the analysis of grid parity, we compare the benchmark price of coal power and the price under the market-oriented mechanism with CPV. The results show that China’s CPV industry will enter the early stage of maturity from 2020 onwards; with the help of benchmark investment, the grid parity of CPV may be achieved in 2022 at the earliest and 2025 at the latest. After 2025, the photovoltaic electricity price will be generally lower than the coal electricity price under marketization. By 2030, CPV power generation costs will reach US $0.05/kWh, the accumulative installed capacity will exceed 370 GW, and the uncertainties will lead to a cumulative installed gap of nearly 100 GW.

Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes

In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics that govern real-time price competition in electronic marketplaces. Employing this model, an empirical study was performed on the price trends of smartphone models on the major electronic sales platforms of the Italian market. The proposed model detects real-time price variations in single vendors, based on the variations of their direct competitors. The statistical method adopted in this analysis may be useful for e-commerce companies that conduct market analyses of competitors’ pricing strategies.

Selection Criteria for Sustainable Suppliers in the Supply Chain of Copper Mining in Chile

The objective of this research was to identify the criteria for the selection of sustainable suppliers in the supply chain of copper mining located in the Antofagasta region in Chile, through the information obtained in the application of a survey managers mining companies in 2018. The research results show that mining companies use economic, environmental and social criteria in the selection of sustainable suppliers, which are consistent with the research carried out by Dickson (1966) and Zimmer, Frohling and Schultmann ( 2016). However, the differentiation that mining companies make in the evaluation of criteria when it comes to suppliers of goods or service providers stands out as an important finding. In the case of suppliers of goods, economic criteria are valued more preferably, such as: quality, delivery on time, price, historical performance and previous sales. On the other hand, when it comes to service providers, environmental and social criteria, such as: occupational health and safety management and environmental management, are valued with greater importance.

A Real-Time Electricity Price Decision Model for Demand Side Management in Wind Power Heating Mode

The problem of wind power curtailment (WPC) during winter heating periods in China’s “Three-North regions” is becoming worse. Wind power heating, though being an effective way to increase wind power consumptions, is constrained by high electric heating costs under a peak-to-valley electricity price pattern. This study develops a real-time price (RTP) decision model which adjusts the time-varying RTPs within an acceptable range of heating users based on the WPC distribution over a particular dispatch day. The lower RTPs accompanying the higher WPC can guide the electric heating user side equipped with regenerative electric boilers (REBs) to actively increase REB imports to absorb additional wind generation. Then, the demand side response using REBs under the RTP scheme is optimized to minimize the total heating cost met by electric heating users while assisting in the large-scale wind generation accommodation. The total heating costs and WPC reductions under different heating scenarios are compared and discussed alongside the effectiveness of the RTP-based demand side management in terms of reducing the WPC and heating costs and increasing the feasibility of wind power heating during winter heating periods.