Vol 6. No. 1, March 2013 African Journal of Computing & ICT © 2013 Afr J Comp & ICT – All Rights Reserved - ISSN 2006-1781 www.ajocict.net
Stochastic Optimization Techniques as Effective Tools to Load Forecasting and Scheduling Using Distributed Energy Resources (DERs) C.G. Monyei Department of Electrical and Electronic Engineering University of Ibadan
[email protected]
ABSTRACT Curbing incessant and erratic power supply to halls of residence within the University of Ibadan Campus has been an impetus that has led to an upsurge in the number of proposals all geared towards providing solution to this critical problem. One of such proposals opines the design of a virtual power plant (VPP). In proposing such, the author seeks to address the problem on a double approach – tackle the erratic power supply and reduce carbon footprints. The proposal in achieving these aims takes advantage of the flexibility of Distributed Energy Resources (DERs), advancements in Information and Communications Technology (ICTs) and the stochastic nature of evolutionary algorithms (EAs) and artificial intelligence (AI) in creating a frame work for interaction between these components, the end users of electricity and the generation/distribution end. The crucial property of electricity being toyed with in the proposal is the ability of electricity to move in both directions depending on existing potential difference. A problem arising from this brilliant proposal though is the fact that loads have not been grouped or biased. The intermittent and stochastic nature of renewables limits their application to certain loads within the halls as such critical loads have to be connected to the school grid for uninterrupted supply. These loads could range from medical to cooking points. This paper seeks to address this issue of load biasing while taking advantage of stochastic optimization techniques in scheduling loads for supply and forecasting demand. The author in attempting to do this hopes to improve quality of supply and optimize demand among students within Independence Hall by suggesting creation of incentives, data mining to observe if a pattern exists which to a great extent mirrors students behavior and other EA tools which would prove useful. Keywords: virtual power plant, evolutionary algorithms, artificial intelligence, distributed energy resources, stochastic . African Journal of Computing & ICT Reference Format C.G. Monyei (2013 Stochastic Optimization Techniques as Effective Tools to Load Forecasting and Scheduling Using Distributed Energy Resources (DERs). Afr J. of Comp & ICTs. Vol 6, No. 1. Pp 180-184
1. INTRODUCTION The University of Ibadan has not fared well in recent times as regards power supply to its resident communities spanning the halls of residence, staff residential quarters, the Abadina Community etc. [12,13,14]. This poor power supply is no doubt connected to poor power generation and an obsolete transmission/distribution network currently plaguing Nigeria [9]. The University Management in tackling this menace has been exploring alternative means of power generation like renewables, while also increasing the capacity of its diesel generators [9]. These giant strides no doubt will yield no much change as they do not seek to promote a culture of efficiency and responsibility on the part of end-users as regards safe and efficient use of electricity. Monyei [9] in trying to correct this problem proposed the design of a virtual power plant for the corridor lighting system of Independence Hall as a pilot project.
This sought to introduce some level of intelligence in the corridor lighting system and also provide a case study from which relevant data could be extrapolated and used in designing a bigger VPP which could gradually be introduced into halls’ power network and then gradually the school. While acknowledging the brilliance and importance of such a proposal, this author attempts to identify some loopholes and design flaws expected in fully implementing the proposal while also providing solutions to them. As can be surmised from Georgios et al [10], the variable nature of generation of DERs is another uncertainty that has to be addressed real time in incorporating DERs into an existing grid. Their view is further reified by Sarvapali et al [4] who buttress the fears associated with intermittent renewable energy sources while positing that smart grids must be able to access these in supplying additional electricity. [1][2][3][5][6][7][8][11] also opine the earlier held notion on the intermittent nature of DERs. 180
Vol 6. No. 1, March 2013 African Journal of Computing & ICT © 2013 Afr J Comp & ICT – All Rights Reserved - ISSN 2006-1781 www.ajocict.net
2. INTRODUCTION OF ADDITIONAL DESIGN CONCEPTS From the foregoing therefore, the need arises for the introduction of additional design concepts to the earlier proposed model of Monyei [9]. In enhancing the earlier proposed design concept, the nature of loads available in the hall of residence is taken into account. This enhanced model calls for the discrimination of loads into different distribution centres for effective utilization of installed DG units and the University Power Supply Scheme. Table 1 below gives an estimate of on-peak load and average off-peak load consumption for five days (Monday-Friday) in Independence Hall, University of Ibadan. Table 1: School days load consumption estimate for Independence Hall Discriminated Time of the day to Peak load come on (KW) Lighting points 5pm – 7am 16.00 Monday - Friday Cooking points 24 hors 164.00 Corridor lighting 5pm – 7am 2.80 others 24 hours 45.00 Total (KW) 227.80
consumption
Average off-peak consumption (KW) 15.98 159.00 2.78 41.00 218.76
Table 2 below gives an estimate of on-peak load and average off-peak load consumption for weekends (Saturday and Sunday) in Independence Hall, University of Ibadan. Table 2: Weekend load consumption estimate for Independence Hall Discriminated Time of the day to load come on Lighting points 24 hours Saturday Cooking points 24hours Corridor lighting 5pm – 7am others 24 hours Total (KW) Lighting points 24 hours Sunday Cooking points 24 hours Corridor lighting 5pm – 7am Others 24 hours Total (KW) From the foregoing therefore, fig 1 below is proposed. The proposed figure 1 gives a pictorial view of the intended load discrimination. As can be observed, the loads have been divided into the component structures analyzed in tables 1 and 2 above namely: lighting points, cooking points, corridor lighting and others. The lighting points division represents all points of illumination within each room of residence within the hall. The cooking points refer to the access points provided by the University Management for students to access larger currents through the use of electric cookers and heaters. The corridor lighting (points) refer to illumination provided for hall ways, corridors, kitchenettes, bathrooms and stairways. The designation others refers to all other permitted points of accessing electricity mainly within the rooms for lighter use. In coming up with this model, it is assumed that appropriate safety regulations and precautions such as fuse ratings, cable selection etc. have all been taken into consideration.
Peak consumption (KW) 16.50 170.00 2.80 50 239.30 16.30 165.00 2.80 47.00 231.10
Average off-peak consumption (KW) 16.25 168.00 2.78 49.55 236.58 16.16 163.00 2.78 45.00 226.94
3. DESIGN ISSUES Figure 2 shows the proposed connection of the loads to available power sources – the grid and installed DG Units. The connection being proposed is capable of intentional islanding in the event of power failure from the grid or a fault in the grid supply. As can be surmised from fig 2, the loads are connected in such a way that the installed DG Units are capable of meeting in excess the demand from the lighting points and the corridor lighting point as shown below. Let n be the total number of installed DG Units ………………………….…………… (1) n Let ∑Pdgi(t) be the total DG Capacity in Kilowatts i=1 at time t ..………………………… (2) Let Plp(t) be the power demand in Kilowatts from lighting points at time t………….... (3)
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Vol 6. No. 1, March 2013 African Journal of Computing & ICT © 2013 Afr J Comp & ICT – All Rights Reserved - ISSN 2006-1781 www.ajocict.net
Let Pcl(t) be the power demand in Kilowatts from corridor lighting points at time t…... (4)
The connection is thus done such that n n ∑Pdgi (t) – Plp (t) – Pcl (t) ≥ (0.1 *∑ Pdgi (t)) i=1 i=1 .................................................................. (5) Equation 5 being very crucial allows for frequency control and voltage regulation in the event of
To cooking points
intentional islanding. Incorporating this design concept thus introduces some measure of intelligence and provides for extended intelligent supply in the event of a grid trip off. In analyzing figure 2, it is important to note that switch 1 is only closed when grid supply is available and DG units are incapable of meeting current demands due to their capacity being either low, their being faulty or out of service (maintenance). Switch 2 is closed only when the DG Units are capable of meeting current demands and have their battery storage fully charged. The demand shifted from the other loads – others and cooking points is analyzed subsequently.
Legend
To corridor lights
To corridor lights To cooking points To others To others
To lighting points
To lighting points
Multidistribution panel
Fig 1: Load Discrimination Let Pcp (t) be the power demand from the cooking points in kilowatts at time t .. (6) Let Pop (t) be the power demand from other points in Kilowatts at time t ……… (7) Let Pg (t) be the grid supply available at time t in Kilowatts ……………………. (8) n n Pa (t) = ∑Pdgi (t) – Plp (t) – Pcl (t) ≥ (0.1 *∑ Pdgi (t)) …………………………... (9) i=1 i=1 Where Pa (t) is the available excess power from DG Units. Load displaced by grid Pb (t) in Kilowatts is thus given as: Pb (t) = Pcp (t) + Pop (t) - Pa (t) …………………………………….……….…. (10) Power savings from the grid in Kilowatts is also computed as: Pg (t) – Pb (t) ………………………………………………………………….. (11)
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Vol 6. No. 1, March 2013 African Journal of Computing & ICT © 2013 Afr J Comp & ICT – All Rights Reserved - ISSN 2006-1781 www.ajocict.net
Control signal
From DG Units
Legend
From grid
To corridor lights
1 To cooking points To others
2
To lighting points
Synchronizer Intelligent switch Fig 2: Proposed Load Connection to Grid and DG Units Figures 3 and 4 shown subsequently are block diagrams showing the generation of control signal input into figure 2. As can be observed from figure 3, the voltage level as well as other parameters of the installed DG Units are checked against a reference regularly within a time frame t with the corresponding error signal generated passed on to an analyser which generates the relevant signal for transmission to figure 2 through wired or wireless connection. It is important to note here that the safety of the signals being transmitted be guaranteed and proven immune from hackers if wireless communication is to be used. Figure 4 on the other hand generates relevant signals which instruct the power system to either go into an islanding state or trip off connection from one supply source or both. The transmitted signal is usually the difference between every available power supply source (grid and DG Units) at any time t and the load/demand at that same instant of time t.
Sum block
+ Vref
Sum block
+ Analyser
Control signal
VDG
Fig 3: generating state of DG Units control signal
As can be observed from figures 1 – 4 above, incorporating these concepts not only shore up the reliability and safety of the earlier proposed model but allows for more control of loads in terms of scheduling and displacement. In utilizing the varied benefits of artificial intelligence (AI) and other EA tools, the use of Artificial Neural Networks (ANNs) and Genetic Algorithm (GA) is thus proposed for the purposes of load forecasting and scheduling purposes.
Pgrid
Analyser
Control signal
+ PDG
-
Load
Fig 4: generating control signal during excess load demand
A single layer, five inputs, one output forward neural network with no feedback is proposed for use in mining the available data in a bid to forecasting subsequent load demands. The generated values are then checked against actual values and adjustment for errors is then carried out on the ANN. This proposition is to be incorporated into an earlier proposed work by Monyei [15] in which GA is used in optimally allocating students into the halls of residence and calculating the fees due each student in defraying the costs associated with increasing the capacity of the DG Units each year.
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Vol 6. No. 1, March 2013 African Journal of Computing & ICT © 2013 Afr J Comp & ICT – All Rights Reserved - ISSN 2006-1781 www.ajocict.net
4. RESULT AND CONCLUSION As opined by Monyei [15], saving time and drudgery as well as the flexibility of the GA in generating valid values make it the optimal tool in load forecasting and scheduling. The combined use thus of the ANN and GA in effectively forecasting and scheduling load increases the flexibility, intelligence and extent of these algorithms in arriving at a stable and optimal solution. This design is thus recommended for adoption in designing a more improved virtual power plant for the halls of residence within the University of Ibadan.
REFERENCES [1] Alberto J. L., Tim M., Ray Z., Carlos E. M. and Lindsay, A. (2012) “Alternative Mechanisms for Integrating Renewable Sources of Energy into Electricity Markets” retrieved from http://ieeexplore.proxy.library.carleton.ca/ [2] Anderson L., Galloway S. and Stephen B. (2012) “Assessment of the Impact of different energy mixes in local decentralized energy networks” in Journal of Power and Energy retrieved from http://pia.sagepub.com/ at Carleton University. [3] Pudjianto D., Ramsay C. and Strbac G. (2008) “ Micro-grids and Virtual Power plants: Concepts to support the integration of Distributed Energy Resources.” Retrieved from http://ieeexplore.proxy.library.carleton.ca/ p.731. [4] Sarvapali D., Perukrishnen V., Alex R. and Nicholas R. (2012) “Putting the ‘smarts’ into the smart grid: A Grand challenge for Artificial Intelligence” in Communication of the ACM, p.86 retrieved from http://ieeexplore.proxy.library.carleton.ca/ [5] Schafer A. and Moser A. (2012) “Dispatch Optimization and Economic Evaluation of Distributed Generation in a Virtual Power Plant.” Retrieved from http://ieeexplore.proxy.library.carleton.ca/ [6] Petersen M., Bendsten J. and Stoustrup J. (2012) “Optimal Dispatch Strategy for Agile Virtual Power Plant” in the 2012 American Control Conference, Fairmont Queen Elizabeth, Montreal, Canada, retrieved from http://ieeexplore.proxy.library.carleton.ca/ [7] Such M. C. and Hill C. (2011) “Battery Energy Storage and Wind Energy Integrated into the Smart Grid” retrieved from http://ieeexplore.proxy.library.carleton.ca/
[8] Li D., Jayaweera S. and Abdallah C. T. (2012) “Uncertainty Modeling and Stochastic Control Design for Smart Grid with Distributed Renewables” retrieved from http://ieeexplore.proxy.library.carleton.ca/ [9] Monyei C. G. (2012) “Towards Sustainable Energy Development Using Virtual Power Plants” in the African Journal of Computing and ICT, retrieved from http://ajoict.net/ pp119-123. [10] Chalkiadakis G., Robu V., Kota R., Rogers A. and Jennings N. R. “Cooperatives of Distributed Energy Resources for efficient Virtual Power Plants” Proc. of 10th Int. Conf. on Autonomous Agents and Multi-agent Systems – Innovative Applications Track (AAMAS 2011), Tumer, Yolum, Sonenberg and Stone (eds.), May, 2–6, 2011, Taipei, Tai-wan, pp. 787-794. [11] Kok J. K., Warmer C. J. and Kamphuis I. G. (2005) “Multiagent Control in the Electricity Infrastructure” _____ retrieved from http://ieeexplore.proxy.library.carleton.ca/ pp 75-82. [12] http://www.channelstv.com/home/2012/04/26/Universit y-of-ibadan-spends-n40-million-on-power-v-c/ [13] http://www.ui.edu.ng/content/invitation-tender-o/ [14]http://Campusheathq.com/update-finallyuniversity-of-ibadan-calls-off-strike-resumption-dateannounced/ [15] Monyei C. G. (2012) “Adaptive Genetic Algorithm for Students’ Allocation to Halls of Residence Using Energy Consumption as Discriminant” (about to be published).
Author’s brief Monyei Chukwuka (B’ 1989) is a final year student in the Electrical and Electronic Engineering Department of the University of Ibadan, Ibadan, Nigeria. His interests centre on distributed generation as a means to reducing carbon emissions and improving energy efficiency and artificial intelligence in a bid to improving the flexibility and reliability of distributed energy resources. His final year project centers on modeling a virtual power plant for the corridor lighting system of a hall of residence within the University of Ibadan Campus. He has delivered a paper on this at the Nigerian Society of Engineers (NSE) Harmony Conference, 2012 and has several published works. He can be reached at
[email protected]
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