Long-Term Enterprise Planning and Performance Control in Competitive Markets
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Abstract
Long-term enterprise planning and performance control are essential managerial processes for organizations operating in competitive markets characterized by uncertainty, changing demand, strategic rivalry, technological development, and fluctuating costs. Organizations cannot sustain their competitive position through short-term operational decisions alone; they require an integrated planning system that connects strategic objectives, resource allocation, market participation, risk management, and performance evaluation. Competitive electricity markets provide a particularly useful setting for examining these relationships because generating companies, retailers, and large consumers must make interdependent decisions concerning bidding, procurement, pricing, capacity utilization, and financial returns. The present paper examines how long-term enterprise planning can be integrated with performance control to support organizational sustainability and competitiveness.
The study adopts a conceptual and analytical research approach based exclusively on the twenty references supplied for this paper. The literature is examined in relation to competitive bidding, electricity-market restructuring, game-theoretic decision-making, optimization algorithms, coordinated market participation, and agent-based modelling. The analysis uses these themes to develop an integrated enterprise planning and performance control framework. The framework connects environmental assessment, strategic objective formulation, resource planning, market strategy, operational execution, performance measurement, and corrective action through a continuous feedback process.
The analysis indicates that enterprise planning and performance control should not be treated as separate managerial functions. Long-term plans establish the direction of organizational development, whereas performance controls determine whether operational decisions remain consistent with strategic objectives. Optimization techniques can improve the allocation of resources and the formulation of bidding strategies, but their effectiveness depends on assumptions about market behaviour, available information, operational constraints, and the quality of input data. Similarly, game-theoretic and agent-based approaches can help managers understand strategic interdependence, although their predictions remain sensitive to model design and changing competitive conditions.
The paper proposes a balanced performance system that combines financial, operational, strategic, and risk-related indicators. It also explains how scenario analysis, periodic reviews, and feedback mechanisms can help enterprises revise plans when market conditions diverge from expectations. The principal contribution is a conceptual integration of long-term planning, competitive decision-making, and performance control. The framework may assist enterprises in improving strategic consistency, resource efficiency, risk responsiveness, and long-term value creation. However, because the study does not include primary data or empirical testing, the proposed relationships should be validated through future industry-specific research.
References
A. Bhardwaj et al., “Optimal bidding strategy for profit maximization of generation companies under step-wise bidding protocol ”, Int. J. Eng. Technol, vol. 9, no. 2, pp. 797 - 805, 2017.
A. J. Conejo and R. Sioshansi, “Rethinking restructured electricity market design: Lessons learned and future needs ”, Int. J. Elect. Power Energy Syst., vol. 98, pp. 520 - 530, 2018.
Abhilipsa Sahoo, Prakash Kumar Hota, “Impact of renewable energy sources on modelling of bidding strategy in a competitive electricity market using improved whale optimization algorithm ”, IET Renewable Power Generation, October 2020.
David, A.K. : “Competitive bidding in electricity supply ”, IEE Proc., Gener. Trans. Dist., 1993, 140, ( 5 ), pp. 421–426.
David, A.K., and Wen, F. “Strategic bidding in competitive electricity souks: a literature survey ”. IEEE PES Summer Meeting, 2000, vol. 4, pp. 2168–2173.
David, A.K., and Wen, F. : “Optimally co-ordinated bidding strategies in energy and ancillary service souk ”, IEE Proc., Gener. Tran.. Dis., 2002, 149, ( 3 ), pp. 331–338.
David, A.K., and Wen, F. : “Strategic bidding for electricity supply in a day-ahead energy souk ”, Electr. Power Syst. Res., 2001.
David, A.K., and Wen, F.S. : “Optimal bidding strategies for competitive generators and large consumers ”, Elect..Power. Syst., 2001, 23, ( 1 ), pp. 37–43.
Ferrero, R.W., Ramesh, V.C., and Shahidehpour, S.M. : “Transaction analysis in deregulated power system using game theory ”, IEEE Trans. Power Syst., 1997, 12, ( 3 ), pp. 1340–1347.
Fleten, S.-E., and Pettersen, E. : “Constructing bidding curves for a price-taking retailer in the Norwegian electricity souk ”, IEEE Trans. Power Syst., 2005, 20, ( 2 ), pp. 701–708.
Gan K..S., Anthony P. and Teo J. Mutation rate in the evolution of bidding strategies, The 3rd International Symposium on Information Technology 2008.
Gross, G., and Finlay, D.J. : “Optimal bidding strategies in competitive electricity souks ”, Proc. 12th Power System Computation Conf., August 1996, pp. 815–823.
Jidong Wang, Jiahui Wu and Yanbo Che, “Agent and system dynamics-based hybrid modeling and simulation for multilateral bidding in electricity market[J] ”, Energy, vol. 180, 2019.
Kumar, J.V., Pasha, S.J., Kumar, D.V. “Strategic bidding in deregulated market is using particle swarm optimization ”, in: 2010 Annual IEEE India Conference (INDICON), Kolkata, 2010.
P. Jain and A. Saxena, “An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market ”, Eng. Sci. Technol. Int. J., vol. 22, no. 4, Aug. 2019
P. Kiran, K.R.M. Vijaya Chandrakala, “New interactive agent based reinforcement learning approach towards smart generator bidding in electricity market with micro grid integration ”, Applied Soft Computing Journal ( 2020 ).
Song, H.L., Liu, C.C., and Lawree, J. : “Decision making of an electricity suppliers bid in a spot souk ”. Proc. IEEE Power Engineering Society Summer Meeting, 1999, vol. 1.
Torre, S.D., Conejo, A.J., and Contreras, J. : “Finding multi-period Nash equilibrium in pool- based electricity souks ”, IEEE Trans. Power Syst., 2004, 19, ( 1 ), pp. 643–651.
Ugedo, A., Lobato, E., Franco, A., Rouco, L., Fernández-Caro, J., and Chofr, J. : “Strategic bidding in sequential electricity souks ”, IEE Proc. Gener. Tran. Dis., 2006, 153, ( 4 ).
Zhang, D., Wang, Y., and Luh, P.B. : “Optimization based bidding strategies in deregulated souk ”. Proc. IEEE PES Power Industry Computer Applications Conf., 1999, pp. 63–68.
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