Energy Management for Grid-connected Micro Grid with Renewable Energies and Dispatched Loads

• Autorzy: Liu Y. , Jiang C. , Shen J. , Zhou X.

Warianty tytułu

PL

Zarządzanie energią w sieci typu microgrid z rozproszonym obciążeniem i odnawialnymi źródłami energii

• Języki publikacji: EN

Abstrakty

EN

This paper focused on energy management program for grid-connected micro grid with renewable generation and electric vehicles. The proposed program, including energy purchase and self-scheduling problems, aimed to minimize energy cost based on forecasting of loads, prices and renewable generations and was solved with genetic algorithm and pattern search methods. Furthermore, it adopts the expectation model and Monte Carlo methods to solve the uncertainty problems. Simulation results proved the effectiveness of the proposed program.

PL

Analizowano zarządzanie energią w sieci typu microgrid. Celem jest minimalizacja kosztów bazująca na przewidywaniu obciążenia. Wykorzystano algorytmy genetyczne oraz metodę Monte Carlo.

Słowa kluczowe

EN

energy management; renewable energy; electrical vehicle

PL

pojazdy elektryczne; energia odnawialna

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 5b
• Strony: 87--92
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Liu Y.

autor

Jiang C.

autor

Shen J.

autor

Zhou X.

• Department of Electrical Engineering, Shanghai Jiaotong University

Bibliografia

• [1] Shinji, T., Sekine, T., Akisawa, A., Kashiwagi, T., Fujita, G., Matsubara, M., Reduction of power fluctuation by distributed generation in micro grid. Electrical Engineering in Japan, 163 (2008), No.2, 22-29.
• [2] Lee, P.K., Lai, L.L., Smart Metering in Micro-Grid Applications. 2009 Ieee Power & Energy Society General Meeting, (2009), 1-5.
• [3] Strbac, G., Kirschen, D., Assessing the competitiveness of demand-side bidding. Ieee Transactions on Power Systems, 14(1999), No.1, 120-125.
• [4] Zare, K., Moghaddam, M.P., El Eslami, M.K.S., Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology. Energy, 35(2010), No.7, 2999-3007.
• [5] Philpott, A.B., Pettersen, E., Optimizing demand-side bids in day-ahead electricity markets. Ieee Transactions on Power Systems, 21(2006), No.2, 488-498.
• [6] Das, D., Wollenberg, B.F., Risk assessment of generators bidding in day-ahead market. Ieee Transactions on Power Systems, 20(2005), No.1, 416-424.
• [7] Oh, H., Thomas, R.J., Demand-side bidding agents: Modeling and simulation. Ieee Transactions on Power Systems, 23(2008), No.3, 1050-1056.
• [8] Obara, S., Energy Cost of an Independent Micro-grid with Control of Power Output Sharing of a Distributed Engine Generator. Journal of Thermal Science and Technology, 2(2007), No.1, 67-78.
• [9] Mohamed, F.A., Koivo, H.N., System modelling and online optimal management of MicroGrid using Mesh Adaptive Direct Search. International Journal of Electrical Power & Energy Systems, 32(2010), No.5, 398-407.
• [10] Brooks, A., Lu, E., Reicher, D., Spirakis, C., Weihl, B., Demand Dispatch. Ieee Power & Energy Magazine, 8(2010), No.3, 20-29.
• [11] Zareipour, H., Canizares, C.A., Bhattacharya, K., Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis. Ieee Transactions on Power Systems, 25(2010), No.1, 254-262.
• [12] Li, Y.Z., He, L., Nie, R.Q., Short-term Forecast of Power Generation for Grid-Connected Photovoltaic System Based on Advanced Grey-Markov Chain. Iceet: 2009 International Conference on Energy and Environment Technology, 2, (2009), 275-278.
• [13] Reikard, G., Using Temperature and State Transitions to Forecast Wind Speed. Wind Energy, 11(2008), No.5, 431-443.
• [14] Alsumait, J.S.; Qasem, M.; Sykulski, J.K.; Al-Othman, A.K., An improved Pattern Search based algorithm to solve the Dynamic Economic Dispatch problem with valve-point effect. Energy Conversion and Management, 51(2010), No.10, 2062-2067.