Dynamic Demand Management Research Articles

Dynamic demand management and online tour planning for same-day delivery

For providers to stay competitive in a context of continued growth in e-retail sales and increasing customer expectations, same-day delivery options have become very important. Typically, with same-day delivery, customers purchase online and expect to receive their ordered goods within a narrow delivery time span. Providers thus experience substantial operational challenges to run profitable tours and generate sufficiently high contribution margins to cover overhead costs. We address these challenges by combining a demand-management approach with an online tour-planning approach for same-day delivery. More precisely, in order to reserve capacity for high-value customer orders and to guide customer choices toward efficient delivery operations, we propose a demand-management approach that explicitly optimizes the combination of delivery spans and prices which are presented to each incoming customer request. The approach includes an anticipatory sample-scenario based value approximation, which incorporates a direct online tour-planning heuristic. It does not require extensive offline learning and is scalable to realistically sized instances with multiple vehicles. In a comprehensive computational study, we show that our anticipatory approach can improve the contribution margin by up to 50% compared to a myopic benchmark approach. We also show that solving an explicit pricing optimization problem is a beneficial component of our approach. More precisely, it outperforms both a pure availability control and a simple pricing rule based on opportunity costs. The latter idea is one used in other approaches for related dynamic pricing problems dealt with in the literature.

Integration of biocrude production from fast pyrolysis of biomass with solar PV for dispatchable electricity production

Abstract This article analyses the integration of distributed biocrude production facilities using fast pyrolysis with solar photovoltaic (PV) for dispatchable electricity production. The strong growth of intermittent electricity generation from solar PV and wind is leading to a greater need for energy storage at grid scale and dynamic demand management. Various forms of energy storage, including electrochemical (e.g. batteries), mechanical (e.g. flywheels) and gravitational (e.g. pumped-hydro), are being developed. This work studies the issues of integrating fast pyrolysis of biomass to produce biocrude that can be readily stored in tanks and combusted to produce electricity when required to supplement the electricity generation from a solar PV unit to meet an arbitrary energy demand curve. The use of biomass pyrolysis in this application has a range of benefits, including the flexibility to augment intermittent renewables, the integration of more bioenergy into the electricity sector and the creation of commercial quantities of biocrudes that can be refined into renewable transport fuels such as jet fuel for which few other alternatives exist. Biocrudes, especially partially upgraded, can be stored and used when required in engines and gas turbines, making them a suitable fuel for augmenting the intermittent nature of solar and wind projects. The development of the distributed 100% renewable power stations using a mix of biomass/biocrude and solar PV and/or wind would also increase the certainty of supply, knowledge of quality and price of raw biocrudes that can also be used to supply a centralized biorefinery, thereby substantially reducing the risk of investing in new biorefinery capacity.

Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension

We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.

Mean-Field Games and Dynamic Demand Management in Power Grids

This paper applies mean-field game theory to dynamic demand management. For a large population of electrical heating or cooling appliances (called agents), we provide a mean-field game that guarantees desynchronization of the agents thus improving the power network resilience. Second, for the game at hand, we exhibit a mean-field equilibrium, where each agent adopts a bang-bang switching control with threshold placed at a nominal temperature. At equilibrium, through an opportune design of the terminal penalty, the switching control regulates the mean temperature (computed over the population) and the mains frequency around the nominal value. To overcome Zeno phenomena we also adjust the bang-bang control by introducing a thermostat. Third, we show that the equilibrium is stable in the sense that all agents’ states, initially at different values, converge to the equilibrium value or remain confined within a given interval for an opportune initial distribution.

A Stochastic Approach to “Dynamic-Demand” Refrigerator Control

Dynamic demand management is a very promising research direction for improving power system resilience. This paper considers the problem of managing power consumption by means of “smart” thermostatic control of domestic refrigerators. In this approach, the operating temperature of these appliances and thus their energy consumption, is modified dynamically, within a safe range, in response to mains frequency fluctuations. Previous research has highlighted the potential of this idea for responding to sudden power plant outages. However, deterministic control schemes have proved inadequate as individual appliances tend to “synchronize” with each other, leading to unacceptable levels of overshoot in energy demand, when they “recover” their steady-state operating cycles. In this paper we design decentralized random controllers that are able to respond to sudden plant outages and which avoid the instability phenomena associated with other feedback strategies. Stochasticity is used to achieve desynchronization of individual refrigerators while keeping overall power consumption tightly regulated.

Role of Air Travel Demand Elasticities in Reducing Aviation's Carbon Dioxide Emissions

This paper develops a new set of European air travel demand elasticities with data from the International Civil Aviation Organization; a new database was constructed in the context of examining the role for dynamic demand management in reducing aviation carbon dioxide (CO2) emissions. The results indicate that air travel demand appears to be fairly price inelastic, with a fare elasticity of 20.29% in the short run and 20.44% in the long run. As a result, demand management strategies focused solely on increasing air fares may do little to curb demand growth. In contrast, supply restrictions in the form of reduced service frequencies may offer a more effective means of reducing aviation CO2 emissions—a 10% reduction in frequency levels is found to reduce passenger demand by 5% in the short run and 7.6% in the long run—although such restrictions can create negative economic consequences. A combination of supply restrictions and pricing may offer a more balanced solution.

Decentralized random control of refrigerator appliances

AbstractThis paper considers the problem of dynamic demand management of domestic refrigerators. Dynamic demand management is a promising research direction, which aims to improve power system resilience, by dynamically controlling the energy consumption of devices that exhibit thermal energy storage. In this approach, the operating temperature of appliances, and thus their energy consumption, is modified dynamically, within a safe range, in response to mains frequency fluctuations. Such an operating scheme aims at reducing the strain that the demand side introduces in a power network, when the corresponding supply is temporarily disrupted. Previous research has highlighted the potential of this idea for responding to sudden power plant outages. However, deterministic control schemes have proved inadequate as individual appliances tend to “synchronize” with each other, leading to unacceptable levels of overshoot in energy demand, when they “recover” their steady-state operating cycles. In this paper we describe random controllers that are able to respond to sudden plant outages and which avoid the instability phenomena associated with other feedback strategies. Stochasticity is used to achieve desynchronization of individual refrigerators while keeping overall power consumption tightly regulated.