Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage

Short, Michael; Crosbie, Tracey; Dawood, Muneeb; Dawood, Nashwan

doi:10.1016/j.apenergy.2016.04.052

Cited by 39 publications

(20 citation statements)

References 23 publications

(74 reference statements)

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“…Future work includes further representative testing of the proposed approach and its commercialization. In addition, future work will also investigate the integration of other forms of DR assets (e.g., combined heat and power (CHP) plant [27] and smart home appliances [28] under rolling-horizon techno-economic optimization) into the aggregated DR framework.…”

Section: Discussionmentioning

confidence: 99%

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

et al. 2019

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Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

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Section: Discussionmentioning

confidence: 99%

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

et al. 2019

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“…The author concluded in admitting that the UC problem is far from fully solved, and many researchers preferred new hybrid techniques that combine different optimization techniques. An open software components used in short-term load forecasting combined with the unit commitment problem was presented by Short et al [40]. The empirical results showed a reduction in costs and improved efficiency as a result of balancing market interactions in the presence of prediction inaccuracies in the proposed modelling framework.…”

Section: Literature Review On the Unit Commitment Problemmentioning

confidence: 99%

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

et al. 2018

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Short term probabilistic load forecasting is essential for any power generating utility. This paper discusses an application of partially linear additive quantile regression models for predicting short term electricity demand during the peak demand hours (i.e. from 18:. Additionally the bounded variable mixed integer linear programming technique is used on the forecasts obtained in order to find an optimal number of units to commit (switch on or off. Variable selection is done using the least absolute shrinkage and selection operator. Results from the unit commitment problem show that it is very costly to use gas fired generating units. These were not selected as part of the optimal solution. It is shown that the optimal solutions based on median forecasts (Q 0.5 quantile forecasts) are the same as those from the 99 th quantile forecasts except for generating unit g 8c , which is a coal fired unit. This shows that for any increase in demand above the median quantile forecasts it will be economical to increase the generation of electricity from generating unit g 8c . The main contribution of this study is in the use of nonlinear trend variables and the combining of forecasting with the unit commitment problem. The study should be useful to system operators in power utility companies in the unit commitment scheduling and dispatching of electricity at a minimal cost particularly during the peak period when the grid is constrained due to increased demand for electricity.

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“…This approach is known as Smart Grid, where the nodes connections and the managing of the energy distribution are fundamental. In this scenario, the energy storage has a key role in order to maximize the flexibility and the global efficiency of the Smart Grid [3,4]. In particular, the thermal energy storage is able to extend the availability of the energy source even if it is not constant along the day, as the solar energy source in a Concentrated Solar Plant is (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Convective Effects in a Latent Heat Thermal Energy Storage

Fornarelli

Camporeale

Fortunato

2019

Heat Transfer Engineering

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Convection within a latent heat thermal energy storage (LHTES) shell-and-tube device filled with phase change material (PCM) has been studied by means of numerical simulations. Both, the heat transfer fluid and the PCM mass, momentum and energy equations are solved and coupled with a conjugate heat transfer model. The study highlights three specific zones within the PCM: the top convective-dominated part, the curvilinear solid-liquid interface, and the bottom conductive-dominated part. The PCM melts from the top to the bottom, therefore the main mechanism of melting appears to be confined in the top part of the solid PCM. However, the flow details reveal a convective cell that includes the whole melted PCM from the top to the bottom of the PCM enclosure. Even though the problem is widely studied by means of experiments and numerical simulations, here the convective flow has been studied quantitatively. During the melting phase the viscous and thermal boundary layers at the walls has been reported at different heights from the bottom of the device. Results show in detail the phenomenology of the melting process within a shelland-tube LHTES supporting the development of design solutions that could enhance the heat transfer of such device.

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Load forecasting and dispatch optimisation for decentralised co-generation plant with dual energy storage

Cited by 39 publications

References 23 publications

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

Convective Effects in a Latent Heat Thermal Energy Storage

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