Blockchain and AI Technologies for Prosumers in Smart Grids: A Comprehensive Review

2.1 Carbon Pricing Mechanisms

Carbon pricing through emissions trading systems or carbon taxes incentivizes the transition from fossil-fuel based generation to renewable sources. These mechanisms internalize the environmental costs of carbon emissions, making renewable energy more economically competitive.

2.2 Regulatory Support Structures

Regulatory frameworks must evolve to accommodate bidirectional energy flows and recognize prosumers as active participants in energy markets. This includes net metering policies, feed-in tariffs, and market access regulations.

3.1 Peer-to-Peer Energy Trading

Blockchain enables direct energy trading between prosumers without intermediaries, reducing transaction costs and increasing market efficiency. Smart contracts automate settlement processes and ensure transaction integrity.

3.2 Decentralized Market Structures

Decentralized energy markets built on blockchain technology provide greater resilience, transparency, and accessibility compared to traditional centralized market structures.

4.1 State Monitoring and Prediction

Machine learning algorithms, particularly deep learning models, enable accurate forecasting of renewable generation, load patterns, and market prices. These predictions are essential for optimal system operation and energy trading decisions.

4.2 Optimal Decision Making

Reinforcement learning and optimization algorithms support real-time decision making for energy scheduling, storage management, and market participation strategies.

5.1 Mathematical Formulations

The optimal energy scheduling problem for prosumers can be formulated as:

Where $C_{grid}$ is grid electricity cost, $C_{gen}$ is local generation cost, $R_{sell}$ is revenue from energy sales, and $SOC$ represents state of charge for energy storage.

5.2 Algorithm Implementation

Sample pseudocode for prosumer energy management using reinforcement learning:

class ProsumerAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
    
    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])
    
    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay