A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.

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“…So, one can achieve network optimality by maximizing the profit of each device, with some caveats. (See, e.g., the recent work [24]. )…”

Section: Profit Maximizationmentioning

confidence: 98%

Dynamic Energy Management

Moehle

Busseti

Boyd

et al. 2019

Springer Optimization and Its Applications

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“…The following paragraph summarizes the main topics of the papers published in the Special Issue [1][2][3][4][5]. Authors' geographical distribution of published papers is: 2 from Taiwan, 2 from China, and 1 from Spain.…”

Section: Overviewmentioning

confidence: 99%

“…The authors in [1] show a real-time pricing scheme in order to connect one energy provider (response) and multiple energy hub operators (demand). The paper, by using the Stackelberg game…”

mentioning

confidence: 99%

Smart Management Energy Systems in Industry 4.0

et al. 2020

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“…Wang et al [57] presented a simulation that indicated possible gains from real-time pricing in China. The examples of dynamic pricing for China's case were presented by, e.g., He and Zhang [58] and Ma et al [59]. The adjustments in pricing and implementation of smart grids (including support schemes) can be made by considering the demand response functions involving inefficiency term, as suggested by Broadstock et al [60].…”

Section: Smart Grids In Chinamentioning

confidence: 99%

Sustainability in the Electricity Sector through Advanced Technologies: Energy Mix Transition and Smart Grid Technology in China

Baležentis

Štreimikienė

2019

Energies

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In this paper, we integrate technological, institutional, and financial approaches towards expansion of the smart grid technology. We turn to China as the major producer and consumer of energy with an increasing focus on renewables. China’s electricity market has experienced a number of reforms while trying to address the economic and environmental challenges. In order to identify the basic requirements for the Chinese power sector, we look into the underlying trends of the energy mix, regulatory policies, and financial flows. Understanding these facets allows one to suggest directions for the further development of the Chinese power sector in the light of the smart grid concept. Indeed, the smart grid concept has no strict definition and, therefore, requires simultaneous consideration of multiple factors. The analysis shows that ensuring smooth transfer of electricity across the regions is a quintessential condition for smart grid development and the promotion of renewable energy in China. As the role of the state-owned enterprises still remains the most important one in the sector, private initiatives should be supported. Therefore, financial incentives are needed to guide the shifts in the energy mix and maintain efficient energy generation and consumption in China.

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A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach

Cited by 38 publications

References 34 publications

Dynamic Energy Management

Dynamic Energy Management

Smart Management Energy Systems in Industry 4.0

Sustainability in the Electricity Sector through Advanced Technologies: Energy Mix Transition and Smart Grid Technology in China

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