**4. Simulation Results and Analysis**

The studied network is the 14-bus IEEE network; the network consists of 12 loads, five generators, one uncertainty (wind) source, and three distributed generation (no uncertainty) sources for an hour of the day ahead of the electricity market. It is assumed that the wind would work in two scenarios: high and low. Wind generation in the low scenario is 5 MW and in the high scenario, it is 20 MW. The independent system operator (ISO), as an upstream entity, wants distributed generation sources (DGSs) available in the network to manage network congestion and maximize the social welfare and, ultimately, the ISO contract with the DGSs corresponds to their optimal generation for an hour of the day ahead. In the meantime, the wind turbine is presented as a balancing generator for the system. Without DGSs on the network, locational marginal prices (LMPs) are different in buses; in this case, there is congestion in the network. As shown in Figure 4, with the presence of DGSs, these prices are stabilized and network congestion is managed. As a result, social welfare is increased and operating costs are reduced.

**Figure 4.** Locational marginal prices (LMPs) in different buses, with or without distributed generation sources in the network.

Next, it is been shown what will happen to the power system and electricity market situation with DGSs on the network by injecting different cyber-attacks (false data injection attacks), including load increase (LI), load decrease (LD), DGS price changes (DGPCH), and generator price changes (GPCH). In the FDI attacks, the attacker is able to access the data of the communication links, sensors, local controllers, and central control units so, to simulate the FDI attack, it has been assumed that the attacker can manipulate the data, therefore at the time of the attack, the data has been manipulated to show the attack outcomes.
