Stochastic nonlinear modelling and application of price-based energy flexibility

Junker, Rune Grønborg; Kallesøe, Carsten Skovmose; Real, Jaume Palmer; Howard, Bianca; Lopes, Rui Amaral; Madsen, Henrik

doi:10.1016/j.apenergy.2020.115096

Cited by 36 publications

(9 citation statements)

References 20 publications

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“…In this context, an app such as FEEDME can help in extending the advantages of smart thermostats, a product standardly thought for home-usage, also to non-residential buildings. Moreover, a deep knowledge of the occupants needs connected to the occupants presence patterns can help in predicting building needs and maximising the flexibility potential offered by buildings to the energy grids (Junker et al, 2020(Junker et al, , 2018Molitor et al, 2012). In this context, occupants behaviour models (Calì et al, 2018(Calì et al, , 2016aHaldi et al, 2017;Wolf et al, 2019) might be useful to understand and cluster occupants preferences; those models could be used in the HuiL-based control of buildings, and also within the simulation of buildings' performance.…”

Section: Discussionmentioning

confidence: 99%

Human in the Loop: perceived based control as the key to enhance buildings’ performance

Calı̀¹,

Thilker²,

Specht³

et al. 2021

Building Simulation Conference Proceedings

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Section: Discussionmentioning

confidence: 99%

Human in the Loop: perceived based control as the key to enhance buildings’ performance

Calı̀¹,

Thilker²,

Specht³

et al. 2021

Building Simulation Conference Proceedings

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“…Therefore, the power wholesale price wij provided by generator i to the retailer j can be expressed as Formula (1) (Zhao et al , 2012). In the electricity market, price fluctuation leads to the uncertainty of market demand (Kong et al , 2017; Junker et al , 2020). As the homogeneity of electricity and the competitiveness of the power market, the selling price of power retailers is the same and the selling price has an inverse linear relationship with the total power supply quantity in the consumer market (Oliveira et al , 2013).…”

Section: System Overview and Benchmark Modelmentioning

confidence: 99%

Pricing contract design of a multi-supplier-multi-retailer supply chain in hybrid electricity market

Zhu

Kong

Aisaiti

et al. 2021

IMDS

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PurposeIn the hybrid electricity market consisting of renewable and conventional energy, the generation output of renewable power is uncertain because of its intermittency, and the power market demand is also fluctuant. Meanwhile, there is fierce competition among power producers in the power supply market and retailers in the demand market after deregulation, which increases the difficulty of renewable energy power grid-connection. To promote grid-connection of renewable energy power in the hybrid electricity market, the authors construct different contract decision-making models in the “many-to-many” hybrid power supply chain to explore the pricing strategy of renewable energy power grid-connecting.Design/methodology/approachConsidering the dual-uncertainty of renewable energy power output and electricity market demand, the authors construct different decision-making models of wholesale price contract and revenue-sharing contract to compare and optimize grid-connecting pricing, respectively, to maximize the profits of different participants in the hybrid power supply chain. Besides, the authors set different parameters in the models to explore the influence of competition intensity, government subsidies, etc. on power pricing. Then, a numerical simulation is carried out, they verify the existence of the equilibrium solutions satisfying the supply chain coordination, compare the differences of pricing contracts and further analyze the variation characteristics of optimal contract parameters and their interaction relations.FindingsRevenue-sharing contract can increase the quantity of green power grid-connection and realize benefits Pareto improvement of all parties in hybrid power supply chain. The competition intensity both of power supply and demand market will have an impact on the sharing ratio, and the increase of competition intensity results in a reduction of power supply chain coordination pressure. The power contract price, spot price and selling price have all been reduced with the increase of the sharing ratio, and the price of renewable power is more sensitive to the ratio change. The sharing ratio shows a downward trend with the increase of government green power subsidies.Originality/valueOn the basis of expanding the definition of hybrid power market and the theory of newsvendor model, considering the dual-uncertainty of green power generation output and electricity market demand, this paper builds and compares different contract decision-making models to study the grid-connection pricing strategy of renewable energy power. And as an extension of supply chain structure types and management, the authors build a “many-to-many” power supply chain structure model and analyze the impact of competition intensity among power enterprises and the government subsidy on the power grid-connecting pricing.

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“…Junker et al have [6] proposed a linear function to characterize energy flexibility. The method has recently been generalized to nonlinear systems [7]. In this last paper, the energy flexibility is evaluated with stochastic differential equations describing the change of state-ofcharge (SOC) as a function of the change of power compared to a reference scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

“…' ' w X X X (7) This is a linear state-space model with a single input and a single output (SISO), ΔPe and ΔTi, respectively. It has the corresponding transfer function H(s):…”

Section: Linearization Of the Building Dynamicsmentioning

confidence: 99%

Development of a data-driven model to characterize the heat storage of the building thermal mass in energy planning tools

et al. 2021

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In the one hand, energy planning tools compute the cost-optimal investment in the energy system minimizing life cycle costs (LCC). These tools often consider optimal control. The building (or cluster of buildings) is represented by a node where the time profiles of energy demands are given as inputs. The indoor temperate in buildings is typically not considered and may even be difficult to define for a cluster of buildings. Secondly, to perform optimization, the model of the energy system is often linear (e.g. using MILP). In the other hand, the building thermal mass has proven to be a cheap and large source of energy flexibility. Therefore, there is a need for a linear model of the building thermal dynamics when there is a limited knowledge of the indoor temperature. Consequently, the paper proposes a model that tracks the change of indoor temperature and space-heating power rather than their absolute values: the model focuses on the deviations from the reference energy profiles given as input. This framework gives a simple model that is less dependent on the boundary conditions (i.e. the weather, user behaviour and internal gains). In addition, a second-order model is proposed to characterize the transfer function. The model has only four parameters, which simplifies its identification. The model is validated using detailed building performance simulation, namely IDA ICE, on a Norwegian wooden detached house during demand response (DR).

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Stochastic nonlinear modelling and application of price-based energy flexibility

Cited by 36 publications

References 20 publications

Human in the Loop: perceived based control as the key to enhance buildings’ performance

Human in the Loop: perceived based control as the key to enhance buildings’ performance

Pricing contract design of a multi-supplier-multi-retailer supply chain in hybrid electricity market

Development of a data-driven model to characterize the heat storage of the building thermal mass in energy planning tools

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