High-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources

Kobyliński, Paweł; Wierzbowski, M.; Piotrowski, Krzysztof

doi:10.1016/j.ijepes.2019.105635

Cited by 52 publications

(27 citation statements)

References 31 publications

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“…The above sentence means by installing rooftop PV at the residential level our energy forecasts would become approximately 30% worse (from MAAPE 0.3225 to 0.4168) in residential level, while this amount for the aggregate level is far more intense, near 250% (from MAAPE 0.0807 to 0.2016). Although there are a handful of works in which net energy forecasting at the aggregate level (not residential level) has been done with the similar amount of error [20,21], this is the first time, to the best of our knowledge, that this importance is raised. Indeed, this issue has been unseen mainly because no comparison has been made or even possible to be made between load and net energy forecasting.…”

Section: B Load Vs Net Energy Forecasting-base Lstmmentioning

confidence: 94%

“…Very recently, a handful of works have been carried out, in which the net energy forecasting is discussed, mainly at aggregate level [18][19][20][21]. Sun et al propose a forecasting model at feeder level, which first, estimates the PV penetration, and then, PV forecast is integrated into load forecasting [18].…”

Section: Introductionmentioning

confidence: 99%

“…Application of a similar method is used in [19] for considering different PV penetration scenarios at the aggregate level to achieve an effective demand-side management approach. Kobylinski et al in [20], investigate the short-term net energy forecasting for a micro-neighbourhood, comprising 75 single houses, with 15 minutes' temporal resolution data. Besides, the importance of aggregated net energy forecasting has been shown for a secured energy trading platform [21].…”

Section: Introductionmentioning

confidence: 99%

“…Application of iterative residual blocks (ResBlocks) in deep neural networks has also been illustrated in extracting spatial and temporal correlations [17]. A predictive model based on the artificial neural networks (ANN) has been developed for the net energy forecasting to apply feature selection technique in the context of lag signals [20]. Tang et al in [23] has used joint bi-directional ANN and deep belief network as a hybrid framework for short-term load forecasting, where candidate features are chosen based on the Pearson correlation coefficient.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter

et al. 2020

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Section: B Load Vs Net Energy Forecasting-base Lstmmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter

et al. 2020

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“…Long term online power scheduling is studied in [27] to reduce cost and also compare with short term techniques with time and power flexible loads along with integration of RERs. Load forecasting for MG and neighborhood with high level resolution is proposed in [28] for the availability of intermittent generation.…”

Section: Related Workmentioning

confidence: 99%

Multi Objective Based Framework for Energy Management of Smart Micro-Grid

et al. 2020

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The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and ecofriendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work. INDEX TERMS Control agent, energy market management controller, multi objective grey wolf optimization, home energy management controller, local generation, market management, micro-grid, particle swarm optimization, renewable energy resources

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Performance improvement of solar PV power conversion system through low duty cycle DC‐DC converter

Alagu¹,

Prem²,

Sivaraman³

et al. 2020

Circuit Theory & Apps

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Characteristics like reliability and modularity of solar photovoltaic (SPV) units makes them one of the apt options for generating electric power. However, the input intermittency is their major lacuna, and power electronic circuits are being used to nullify this effect. A solar photovoltaic power conversion process includes a DC-DC converter in the plant side to deliver a fixed DC voltage and a DC-AC converter in the grid side for converting DC voltage in to grid compatible AC voltage. The high duty ratio of typical DC-DC boost converters limits the switching frequency and efficiency. In this article, a way to enhance the performance of a SPV unit using an improved quadratic boost converter (IQBC) is analysed. It can deliver a higher output voltage at lower duty ratio with Perturb and Observe (P&O) algorithm-based Maximum Power Point Tracking (MPPT). A 250 W prototype of IQBC-based Solar PV power conversion system (SPVPCS) is developed, and its performance is compared against conventional and quadratic boost converters and found that the IQBC-based solar power conversion system is efficient and the results are presented. K E Y W O R D S boost converter, duty ratio, improved quadratic boost converter, solar PV conversion, system, voltage gain 1 | INTRODUCTION Renewable energy resources are getting much importance nowadays due to wider exploitation of fossil fuels. 1 Even though there are several renewable energy sources available, wind and solar energy are gaining an edge over the other resources due to their reliability and ability to deliver higher power levels. But it does have a limitation; that is, the input for solar PV system is highly intermittent. This effect of input intermittency can be nullified using power converters. 2,3 DC-DC converters are used to nullify these fluctuations through step up/step down of DC voltage from solar PV panel. Even though higher voltage can be obtained by connecting panels in series, losses due to partial shading and cost will be the drawbacks. 4-6 To achieve voltage boost, a boost converter have to be operated at high duty ratio. While operating the converter in higher duty ratio, issues like high switching voltage stress, limited switching frequency, reverse recovery problem and electromagnetic interference may occur, and also, it affects the efficiency of the converter. 7 As a remedy to the above setbacks, many DC-DC boost converter topologies are proposed recently. 8 Cascaded boost converter gives an opportunity of obtaining high output voltage with less duty ratio. 9,10 Even though it offers individual panel level MPPT, its cascaded structure raises the cost of overall system. 11 SEPIC converter embedded with the MPPT

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High-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources

Cited by 52 publications

References 31 publications

From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter

From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter

Multi Objective Based Framework for Energy Management of Smart Micro-Grid

Performance improvement of solar PV power conversion system through low duty cycle DC‐DC converter

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