Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.

Afghanistan is a key country between energy surplus areas (Central Asian Republics andIran) and energy deficit regions (Pakistan and India). It is in a position that can facilitate and launchregional electricity trade for the benefit of the region also derive significant gains for its own economyfrom energy imports and exports. On the other hand, Afghanistan is endowed with large renewableenergy resources (RERs), which it could exploit not only to satisfy its domestic power demand butalso to earn significant export revenue. This paper firstly explains the methodology and framework forthe power trade and then presents an optimization framework for profit maximization in the short-runtrading and cost minimization in the long-run trading. The proposed methodology is applied to a realcase between Afghanistan and Pakistan. The objective functions, parameters, variables and constraintsare described for both optimization models. System sizing, simulation and optimization are carriedout using genetic algorithm (GA) technique. The results in the short-run model represent optimalityof about 2654 MW electricity export from Afghanistan to Pakistan during summer. Moreover, resultsderived from running long-run model depict that by utilizing its RERs such as solar, wind and hydro,Afghanistan can not only meet its power demand but also can export to Pakistan during its deficitperiods and gain remarkable energy profits.

This paper focuses on the optimal unit commitment (UC) scheme along with optimal power trading for the Northeast Power System (NEPS) of Afghanistan with a penetration of 230 MW of PV power energy. The NEPS is the biggest power system of Afghanistan fed from three main sources; 1. Afghanistan’s own power generation units (three thermal units and three hydro units); 2. imported power from Tajikistan; 3. imported power from Uzbekistan. PV power forecasting fluctuations have been handled by means of 50 scenarios generated by Latin-hypercube sampling (LHS) after getting the point solar radiation forecast through the neural network (NN) toolbox. To carry out the analysis, we consider three deterministic UC and two stochastic UC cases with a two-stage programming model that indicates the day-ahead UC as the first stage and the intra-day operation of the system as the second stage. A binary-real genetic algorithm is coded in MATLAB software to optimize the proposed cases in terms of thermal units’ operation costs, import power tariffs, as well as from the perspective of the system reliability risks expressed as the reserve and load not served costs. The results indicate that in the deterministic UC models, the risk of reserve and load curtailment does exist. The stochastic UC approaches including the optimal power trading are superior to the deterministic ones. Moreover, the scheduled UC costs and reserves are different from the actual ones.

Future, transmission capacity processing capability may become the limit because of the pursuit of economic benefit and increasing renewable energy. Then, the power system is placed in severe conditions than ever. In addition, it increases the likelihood of voltage instability and voltage collapse. Therefore, voltage stability analysis is a major concern in power system operation. Voltage stability has been analyzed in a variety of ways. However, voltage stability analysis considering simultaneously both the active and reactive power in the analysis is less done because of it is difficult analysis. When considering both the active and reactive power at the same time, it is expected that voltage stability analysis is accurate. In this paper, we propose a method to improve the voltage stability of the power system by using the voltage stability index of the active power information and reactive power information of the transmission line. Installing the battery in the load substation to inject the appropriate amount of active and reactive power can improve the voltage stability of the system as shown in the simulation. In addition, it indicates further improvement of voltage stability by optimal active power and reactive power output with battery.