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Stochastic Optimization of Power System Operation in Presence of
Renewable Energy Sources Dumbrava V., Lazaroiu G.C., Bazacliu G., Teliceanu M.
University POLITEHNICA of Bucharest. Bucharest, Romania
Abstract. The liberalization process of the energy market structure determined significant changes in the electric utility industry, from both generating and transmission perspectives, and new generation technologies emerged, deeply influencing the industry profile. Nowadays, based on the governmental incentives and free access to the power systems, the share of renewable energy sources in the bulk power system generation is increasing. With respect to the classical fuel-based sources, the renewable energy sources (like photovoltaic and wind turbines) have an intermittent character determined by the meteorological conditions. Their production cannot be known exactly, but can be forecasted with some degree of accuracy. Thus, within the power system, network measures must be adopted for ensuring its safe operation.
Keywords: renewable energy, electric energy market, stochastic optimization.
Optimizarea stocastica a funcfionarii sistemului energetic in prezenfa surselor de energie regenerabile Dumbrava V., Lazaroiu G.C., Bazacliu G., Teliceanu M.
Universitatea POLITEHNICA Bucure§ti Bucure§ti, Romania
Rezumat. Procesul de liberalizare a structurii pietei energiei a determinat schimbari semnificative in industria de furnizare a energiei electrice din atat perspectiva de generare §i de transmisie, cat §i a noilor tehnologii de generare aparute, influentand profund profilul industriei. Actualmente, datorita stimulentelor guvernamentale §i accesului liber la sistemele energetice, ponderea surselor regenerabile in totalul energiei electrice generate este in cre§tere. in comparable cu sursele clasice pe baza de combustibili, sursele regenerabile de energie (cum ar fi turbinele eoliene §i fotovoltaice) au un caracter intermitent determinat de conditiile meteorologice, productia lor nu poate fi cunoscuta cu exactitate, dar poate fi prognozata cu un anumit grad de precizie. Astfel, in cadrul sistemului de alimentare, trebuie adoptate masuri de retea, in vederea asiguraii unei functionari in conditii de siguranta.
Cuvinte-cheie: energie regenerabila, piata energiei electrice, optimizare stocastica.
Стохастическая оптимизация функционирования энергосистемы при наличии в ней возобновляемых источников энергии Думбравэ В., Лэзэрою Г.К., Базаклиу Г., Теличяну М.
Бухарестский Политехнический Университет Бухарест, Румыния
Аннотация. Либерализация структуры энергетического рынка привела к значительным изменениям в отрасли поставок электроэнергии с учетом перспективы как генерации и передачи, так и появившихся технологий нового поколения, оказала глубокое влияние на профиль отрасли. В настоящее время, благодаря государственным стимулам и свободному доступу к энергетическим системам, доля возобновляемых источников энергии в общем объеме вырабатываемой электроэнергии увеличивается. По сравнению с традиционными источниками на основе углеводородного топлива, использование возобновляемых источников энергии (например, ветряные турбины и фотоэлектрические установки) характеризуется работой с перерывами, определяемыми метеоусловиями, их график работы не может быть известен точно, но может быть предсказан с определенной степенью точности. Таким образом, внутри системы электроснабжения должны быть приняты мероприятия с целью обеспечения надежной и безопасной эксплуатации.
Ключевые слова: возобновляемая энергия, оптимизация, рынок электрической энергии, стохастическая оптимизация.
INTRODUCTION
The electricity market is an economic concept, which has a complex content, and expresses all transactions of purchasing and selling of electrical energy in a specific geographical area [1], [2], [3]. The electricity market has as main function the correlation, through supply and demand, of production with electricity consumption, by fulfilling the buying - selling contracts. Romania has taken the decision to liberalize the electricity market, considering the customers security of supply and therefore the energy system will increase with the development of a coherent electricity market, in which the participants can have benefit from the competition. In order to join the EU, the electricity sector from Romania had not only to comply with the directives and community resolutions, but it must also take action to organize, create and implement procedures and legislative framework and harmonized regulatory which lead towards results provided by these directives. The advantages of the competitive environment take into consideration, the mainly think the direct competition to win, maintain and expand the market section, the effective cost management, free prices formation and not least providing incentives in order to reduce costs and efficient use of resources [4], [5]. The introduction of the competition in the activities that not lead the specific natural monopolies (production and supply of electricity) is beneficial and necessary but clear rules on trade arrangements, the rights and the duties of the competitions, trading mechanisms and establishing collection rights and payment obligations [6].
This paper deals with the study of a system supplied by a set of classical power plants, wind power plants
and photovoltaic installation, all these participating to the electricity markets. A share of generation of each of these power producers is supplying loads with which they have established bilateral contracts. The rest of their generation is used for submitting bids on the day-ahead market. The mathematical model proposed within this paper optimizes the operation of these power producers, determining the power exchanged with the day-ahead market. In addition, the optimization model seek to ensure the operation of the power system determining the reserve requirements and its associated cost in vision of the stochastic character of the renewable sources, under different production scenarios for renewable sources.
2. ELECTRICITY MARKET STRUCTURE
The wholesale electricity is an organized framework where the electricity is purchased by the suppliers from producers and other suppliers, for further selling or for their own use, and also by the network operators to balance their own energy consumption [7], [8]. On the wholesale electricity market, have access to fulfil transactions:
• the producers and the auto-producers of electricity;
• the suppliers;
• the network operators.
The transactions on the wholesale electricity market have the objective of selling and buying: electricity, ancillary services, transport services, green certificates, and distribution services. The connections between the markets are illustrated in figure 1.
D-n D-1 D D+n
►
Fig. 1. The schematic structure of the wholesale market
2.1 Spot Market
Commercial Code.
Day Ahead Market is a component of the wholesale electricity market, on which can realize hourly active transactions with next day delivery. Day Ahead Market is operational in Romania from June 2005. It is a centralized market for buying and selling electricity in short term.
Participating in this market is voluntary and is permitted to all license holders registered at OPCOM for Day Ahead Market. License holders can become participants in the Day Ahead Market if they are:
• electricity producers;
• electricity suppliers;
• network operators who can become participants in the Day Ahead Market and may attend at this market only if they carry out the functions explicitly mentioned in the commercial code.
Network operators have not the right to trade on the Day Ahead Market in order to obtain profit. Moreover, excluding electricity's sales by the TSO to compensate the unplanned exchanges with other TSO transmission system operator has not the right to sell electricity on the Day Ahead Market. On the Day Ahead Market each hour of the delivery day is considered as an independent market. Each transaction corresponds to a supply of electricity at a constant power over the respectively trading range. Each delivery day has 24 consecutive trading intervals and each interval lasts one hour, the first trading period is from 00:00 on the delivery day. Exceptions are the days crossing the municipality during the summer time to winter time and respectively from the winter time towards the summer time when the delivery day is 25 or 23 trading range.
2.2 Centralized Market for Bilateral Contracts Electricity
On the wholesale market of electricity, license holders may conclude bilateral transactions electricity, including bilateral contracts to export or import electricity, in compliance with specific provisions of the Commercial Code of the wholesale electricity and license conditions. Bilateral contracts for buying and selling electricity can be:
• Bilateral contracts with minimum content established by the competent authority (ANRE);
• Deregulated contracts, where the content is determined by direct negotiation between the parties, compliance with the requirements of the
2.3 Bilateral Contracts Market with Continuous Negotiation
Published offers are kind of sale offers or purchase for standard delivery periods and for the daily use of standard power. Participation in the auction sessions is conditioned by the guarantees to tender. On the centralized market for bilateral contracts electricity with continuous negotiation can be traded forward contracts with hourly power 1 MW. The contracts may be concluded for the delivery period of a week, a month, a quarter or a year. In terms of the daily power usage tenders may be: flat offers, half-flat offer, peak hourly offers, and off peak hourly offers.
Characteristic of this market is the possibility of continuous negotiation, starting from an initial offer (opening price) until an agreement between seller and buyer. This market is designed for producers, suppliers and large consumers. To participate at this bidding session, the participants must constitute guarantees of participation.
Each contract includes: the quantity of electricity contracted, contract price, delivery period, the delivery date.
3. STOCHASTIC OPTIMIZATION MODEL
The mathematical optimization model seeks to minimize the operational costs associated with the electricity market requirements facing the stochastic production of wind power plants and photovoltaic installations [9], [10]. The objective function is expressed as:
[MIN]I( Cg ( Pg ) + SU g + SDg )
geGC
+ I -, -KCX, - cgDRgD,, )
+
(1)
I
geGC
I C
( wt — WTsP'" + pv
r , s r, s r, s
—PVSP'"
I vTL
,LOL j shed 'l, s
leLoad
where Cg(Pg) is the power plants cost for producing Pg, SUg and SDg are the start-up and shut-down costs, ns is the scenario probability associated to the renewable energy sources (RES) production, CgU and CgD are the costs of upward/downward reserve (tfgU and RgD), WTr and PVr are the wind and photovoltaic productions of RES, WTrsp'" and PVrsp'"
are the curtailed WT and PV productions, and the last term is the cost associated with load shedding
Zshed l .
I Pg+ I
geGC
reRES
WTr-
- I L 1 I PFh = 0' e Buses
leLoad l beBranch b
(2)
Z(Rg,s Rg,sLi,s
I rU .- RD
geGC
v Tshed + Z L _ +
leLoad
r spill \
+ I (WTr s - W^T I + Z PF, = 0, Vn e Buses, feRES^ ' , s ' , s ' a-»—'- b,s
beBranch
Vs e Seen
PF < PFbmsx, Vb e Branch
(3)
(4)
Pg + RU < P™\ Vg e GC P - RD > 0, Vg e GC
shed
Ls es < Lt, Vl e Load, Vs e Seen w:pf < Wr s, Vr e RES, Vs e Seen
Table 2 - Shares of electrical the units [MW].
energy generated by
The constraints of the model are presented by the each bus balance equation and capacity constraints:
ID GC PV WT GC - RES 60%x (GC-RES) 40%x (GC-RES)
G1 40 40 24 16
G2 152 152 91.2 60.8
G3 40 40 24 16
G4 152 152 91.2 60.8
G5 300 50 250 150 100
G6 591 150 441 264.6 176.4
G7 0 0 0 0
G8 60 60 36 24
G9 155 155 93 62
G10 155 155 93 62
G11 400 100 300 180 120
G12 400 150 250 150 100
G13 300 100 200 120 80
G14 310 310 186 124
G15 350 100 250 150 100
where PFmax is the maximum allowed power flow, Pma is the maximum produced power of the classical generators.
4. CASE STUDY
The case study is applied on the modified IEEE RTS-24 test system [11], where large scale classical generators were replaced with wind power plants and photovoltaic power plants, illustrated in figure 2. The load demand is reported in table 1. The shares of electrical energy generated by the units within the test system used for the bilateral contracts and bided on the spot market are reported in table 2 and table 3.
Table 1 - Demand at each bus
Bus Demand (MW) Bus Demand (MW) Bus Demand (MW)
1 108 7 100 15 317
2 97 8 71 16 225
3 80 9 75 18 333
4 74 10 95 19 281
5 71 13 265 20 240
6 124 14 294
Fig. 2. IEEE RTS-24 bus test system
Table 3 - Shares of electrical energy generated by the units [MW
RES production min med max
WT 0 40% 100%
PV 0 35% 90%
Scenario probability n 20% 50% 30%
The obtained optimization results are illustrated in IEEE RTS-24 bus test system. The value of the
figure 3, showing the produced power by the objective function is 33738 u.m. classical generators and renewable energy sources for supplying the load demand, at each bus of the
a> S o
Q.
400
300
200
100
-100
-200
WT PV
Power classical generators Load
,4,
I I
11 12
14
1-
17 19 20 21 22 23 24
Bus
Fig. 3. Obtained optimization results of the generation units.
0
5. CONCLUSIONS
The paper optimizes the operation of a power system with the classical generation units and renewable energy sources (wind power plants and photovoltaic installation), all these participating to the electricity markets. The loads within the power system has bilateral contracts with the power suppliers, the rest of generation is bided on the spot market. The mathematical model proposed within this paper optimizes the operation of these power producers seeking to minimize the overall costs. In addition, the optimization model seek to ensure the operation of the power system determining the reserve requirements and its associated cost in vision of the stochastic character of the renewable sources, under different production scenarios for renewable sources.
ACKNOWLEGMENTS
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI - UEFISCDI, project number 37BMPNIII-P3 -199/02.09.2016
[10]N. Shuklaa, A.K. Choudhary, P.K.S. Prakash, K. J. Fernandes, M.K. Tiwari, Algorithm portfolios for logistics optimization considering stochastic demands and mobility allowance, Int. J. Prod. Econ., vol. 141, 2013, pp. 146-166
[11] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, E. Billinton, Q. Chen, C. Fong, S. Haddad, S. Kuruganty, W. Li, R. Mukerji, D. Patton, N. Rau, D. Reppen, A. Schneider, M. Shahidehpour, C. Singh, The IEEE reliability test system, IEEE Trans. Power Syst., vol. 14, 1999, pp. 1010-1020
Dumbrava V., assistant professor. Research interests are electrical networks, substations and transformers, power systems, high voltage engineering, optimization methods, power grids. E-mail: v [email protected] Lazaroiu G.C., assistant professor. Research interests are: power quality monitoring and control, energy use, modeling and simulation of power engineering processes, Electricity markets, economic applications of games theory
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