The unit commitment and economic dispatch problems are crucial aspect of power system operation and management. Economic dispatch (ED) involves the optimal allocation of power generation from multiple sources to meet the electrical demands at minimal costs, while unit commitment (UC) entails deciding which generating unit should be online (committed) and which should be offline over a specific time horizon to meet forecasted electricity demand. These problems are considered difficult optimization problems due to the number/type of variable and constraints present. This paper presents a literature survey of classical optimization techniques applied to the UC and ED problems. Comparative numerical results for some standard benchmark problems showcase the performance differences among these methods. Future research directions are identified and discussed, paving the way for solutions with better accuracy, computational efficiency and, ease of implementation.

The unit commitment and economic dispatch problems are crucial aspect of power system operation and management. Economic dispatch (ED) involves the optimal allocation of power generation from multiple sources to meet the electrical demands at minimal costs, while unit commitment (UC) entails deciding which generating unit should be online (committed) and which should be offline over a specific time horizon to meet forecasted electricity demand. These problems are considered difficult optimization problems due to the number/type of variable and constraints present. This paper presents a literature survey of classical optimization techniques applied to the UC and ED problems. Comparative numerical results for some standard benchmark problems showcase the performance differences among these methods. Future research directions are identified and discussed, paving the way for solutions with better accuracy, computational efficiency and, ease of implementation.

Power flow and state estimation are fundamental processes in the operation and planning of power systems. The power flow model integrates network, load, and generation data to calculate voltages, line flows, and system losses across different buses, relying on the resolution of nodal power balance equations. Concurrently, state estimation plays a pivotal role by reconciling actual measurements with modeled values, determining the most probable state of the system. This paper explores power flow and state estimation challenges in power systems engineering, presenting numerical results for an AC power flow problem and two state estimation problems in DC and AC networks. The Newton power flow algorithm is adeptly employed to address the intricacies of the first power flow problem, showcasing its effectiveness in handling the complexities of contemporary power systems. Furthermore, the paper sheds light on the state estimation problems, employing the weighted least squares method to enhance accuracy and reliability. The challenges encountered and solutions proposed provide valuable insights into the intricacies of these critical processes.