The need to introduce fluctuating renewable energy sources (RER) in the energetic mix has not only helped to keep electricity prices down but also has increased the degree of randomness within the electricity spot markets. For suppliers and consumers forecasting the electricity market spot price is resumed in solving a probabilistic problem in which the rapidly fluctuating portion of RER represents an important stochastic variable. This project aims to propose a solution for such a probabilistic problem by establishing a differential equation that describes the temporal evolvement of the intraday electricity market (IEM). This differential equation should produce, as a solution, a probability mass function (PMF) that gives the occurrence probability of any intraday market price and level of consumption at any given time. For instance, let’s assume that there is a PMF that describes the distribution of all possible market states (prices and level of consumption at a given time) within an IEM for a given time frame. The room of relevant states in the price and consumption level’s plane would be automatically determined by the area with higher probabilities of occurrence. Thus, all relevant intraday market’s information will be pooled in a single PMF. With this a more accurate average forecasting price as well as a forecasting error interval can be calculated. Our model will positively affect the use of RER in following ways:

• The marketability of (RER) will be considerably improved. 
• Suppliers and retailers (consumers) can manage their portfolio more flexibly, more accurately and more confidently. 
• The whole power system will benefit from an accurate short-, mid- and long term forecasting. 
• Both the marketing and the physical potential of RER will be used at their maximum in term of efficiency in dispatch, balancing and production capacity.